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Scientific papers citing Dragon

Dragon is widely used in scientific studies. Here you can find a list of some scientific papers (ordered from the most recent ones) which used and cited Dragon. Please write us if you wish to add your scientific publication to the list.


García-Jacas, C.R., Marrero-Ponce, Y., Barigye, S.J., Hernández-Ortega, T., Cabrera-Leyva, L., Fernández-Castillo, A. N-tuple topological/geometric cutoffs for 3D N-linear algebraic molecular codifications: variability, linear independence and QSAR analysis (2016) SAR and QSAR in Environmental Research, 27 (12), pp. 949-975.

Yosipof, A., Shimanovich, K., Senderowitz, H. Materials Informatics: Statistical Modeling in Material Science (2016) Molecular Informatics, 35 (11-12), pp. 568-579.

Semenyuta, I., Kovalishyn, V., Tanchuk, V., Pilyo, S., Zyabrev, V., Blagodatnyy, V., Trokhimenko, O., Brovarets, V., Metelytsia, L. 1,3-Oxazole derivatives as potential anticancer agents: Computer modeling and experimental study (2016) Computational Biology and Chemistry, 65, pp. 8-15.

Levet, A., Bordes, C., Clément, Y., Mignon, P., Morell, C., Chermette, H., Marote, P., Lantéri, P. Acute aquatic toxicity of organic solvents modeled by QSARs (2016) Journal of Molecular Modeling, 22 (12), art. no. 288.

Barycki, M., Sosnowska, A., Gajewicz, A., Bobrowski, M., Wilenska, D., Skurski, P., Gieldon, A., Czaplewski, C., Uhl, S., Laux, E., Journot, T., Jeandupeux, L., Keppner, H., Puzyn, T. Temperature-dependent structure-property modeling of viscosity for ionic liquids (2016) Fluid Phase Equilibria, 427, pp. 9-17.

Hu, B., Kuang, Z.-K., Feng, S.-Y., Wang, D., He, S.-B., Kong, D.-X. Three-dimensional Biologically Relevant Spectrum (BRS-3D): Shape similarity profile based on PDB ligands as molecular descriptors (2016) Molecules, 21 (11), art. no. 1554.

Yang, X., Liu, H., Yang, Q., Liu, J., Chen, J., Shi, L. Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships (2016) Chemosphere, 163, pp. 373-381.

Shar, P.A., Tao, W., Gao, S., Huang, C., Li, B., Zhang, W., Shahen, M., Zheng, C., Bai, Y., Wang, Y. Pred-binding: large-scale protein–ligand binding affinity prediction (2016) Journal of Enzyme Inhibition and Medicinal Chemistry, 31 (6), pp. 1443-1450.

Aissaoui, M., Rachedi, S.A., Mokhnane, A.M., Louzim, H., Djerourou, A. Computational study of quinolone's antibacterial activity using QSAR approach (2016) Research Journal of Pharmaceutical, Biological and Chemical Sciences, 7 (6), pp. 2544-2561.

Zhang, J., Nan, X., Yu, H.-T., Cheng, P.-L., Zhang, Y., Liu, Y.-Q., Zhang, S.-Y., Hu, G.-F., Liu, H., Chen, A.-L. Synthesis, biological activities and structureg'activity relationships for new avermectin analogues (2016) European Journal of Medicinal Chemistry, 121, pp. 422-432.

Kim, D., Lee, S., Kim, M., Lee, E., Yoo, C. Development of QSAR model based on the key molecular descriptors selection and computational toxicology for prediction of toxicity of PCBs (2016) Korean Chemical Engineering Research, 54 (5), pp. 621-629.

Asturiol, D., Casati, S., Worth, A. Consensus of classification trees for skin sensitisation hazard prediction (2016) Toxicology in Vitro, 36, pp. 197-209.

Algamal, Z.Y., Lee, M.H., Al-Fakih, A.M., Aziz, M. High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm (2016) SAR and QSAR in Environmental Research, 27 (9), pp. 703-719.

Zhou, L., Jiang, J., Ni, L., Pan, Y., Yao, J., Wang, Z. Predicting the superheat limit temperature of binary mixtures based on the quantitative structure property relationship (2016) Journal of Loss Prevention in the Process Industries, 43, pp. 432-437.

Jagiello, K., Grzonkowska, M., Swirog, M., Ahmed, L., Rasulev, B., Avramopoulos, A., Papadopoulos, M.G., Leszczynski, J., Puzyn, T. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives (2016) Journal of Nanoparticle Research, 18 (9), art. no. 256, .

Borhani, T.N.G., Afzali, A., Bagheri, M. QSPR estimation of the auto-ignition temperature for pure hydrocarbons (2016) Process Safety and Environmental Protection, 103, pp. 115-125.

Golmohammadi, H., Dashtbozorgi, Z. Prediction of solvation enthalpy of gaseous organic compounds in propanol (2016) Russian Journal of Physical Chemistry A, 90 (9), pp. 1806-1812.

Grzonkowska, M., Sosnowska, A., Barycki, M., Rybinska, A., Puzyn, T. How the structure of ionic liquid affects its toxicity to Vibrio fischeri? (2016) Chemosphere, 159, pp. 199-207.

De, B., Adhikari, I., Nandy, A., Saha, A., Goswami, B.B. Quantitative analysis of essential molecular features of coumarin derivatives with antioxidant activity using chemometric tools (2016) Current Computer-Aided Drug Design, 12 (3), pp. 241-250.

Yuan, J., Yu, S., Gao, S., Gan, Y., Zhang, Y., Zhang, T., Wang, Y., Yang, L., Shi, J., Yao, W. Predicting the biological activities of triazole derivatives as SGLT2 inhibitors using multilayer perceptron neural network, support vector machine, and projection pursuit regression models (2016) Chemometrics and Intelligent Laboratory Systems, 156, pp. 166-173.

Oksel, C., Winkler, D.A., Ma, C.Y., Wilkins, T., Wang, X.Z. Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches (2016) Nanotoxicology, 10 (7), pp. 1001-1012.

Dossin, E., Martin, E., Diana, P., Castellon, A., Monge, A., Pospisil, P., Bentley, M., Guy, P.A. Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during Complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry (2016) Analytical Chemistry, 88 (15), pp. 7539-7547.

Dai, Y.-M., Liu, H., Niu, L.-L., Chen, C., Chen, X.-Q., Liu, Y.-N. Estimation of half-wave potential of anabolic androgenic steroids by means of QSER approach (2016) Journal of Central South University, 23 (8), pp. 1906-1914.

Obradovic, D., Filipic, S., Nikolic, K., Agbaba, D. Optimization of the thin-layer chromatography method for the separation of ziprasidone and its impurities (2016) Journal of Planar Chromatography - Modern TLC, 29 (4), pp. 239-246.

Hodyna, D., Kovalishyn, V., Rogalsky, S., Blagodatnyi, V., Metelytsia, L. Imidazolium ionic liquids as potential anti-Candida inhibitors: QSAR modeling and experimental studies (2016) Current Drug Discovery Technologies, 13 (2), pp. 109-119.

Liu, H., Yang, X., Lu, R. Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin (2016) Chemosphere, 156, pp. 1-7.

Nandi, S. Recent advances in ligand and structure based screening of potent quorum sensing inhibitors against antibiotic resistance induced bacterial virulence (2016) Recent Patents on Biotechnology, 10 (2), pp. 195-216.

Aalizadeh, R., Thomaidis, N.S., Bletsou, A.A., Gago-Ferrero, P. Quantitative Structure-Retention Relationship Models to Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples (2016) Journal of Chemical Information and Modeling, 56 (7), pp. 1384-1398.

Qian, B.-W., Chen, L.-P., Chen, W.-H. Prediction of impact sensitivity of polynitro compounds by artificial neural network based on the genetic algorithm (2016) Hanneng Cailiao/Chinese Journal of Energetic Materials, 24 (7), pp. 644-650.

Sobati, M.A., Abooali, D., Maghbooli, B., Najafi, H. A new structure-based model for estimation of true critical volume of multi-component mixtures (2016) Chemometrics and Intelligent Laboratory Systems, 155, pp. 109-119.

Abbasi, A., Gitifar, V., Setoodeh, P. QSPR strategy to model and analyze surface tension of binary-liquid mixtures: A large-data-set case (2016) Chemometrics and Intelligent Laboratory Systems, 155, pp. 36-45.

Dobchev, D., Karelson, M. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework? (2016) Expert Opinion on Drug Discovery, 11 (7), pp. 627-639.

Heidari, A., Fatemi, M.H. Hybrid Docking-Nano-QSPR: An Alternative Approach for Prediction of Chemicals Adsorption on Nanoparticles (2016) Nano, 11 (7), art. no. 1650078.

Borhani, T.N.G., Saniedanesh, M., Bagheri, M., Lim, J.S. QSPR prediction of the hydroxyl radical rate constant of water contaminants (2016) Water Research, 98, pp. 344-353.

Rezaei, B., Riahi, S. Prediction of CO2 loading of amines in carbon capture process using membrane contactors: A molecular modeling (2016) Journal of Natural Gas Science and Engineering, 33, pp. 388-396.

Nikolic, K., Mavridis, L., Djikic, T., Vucicevic, J., Agbaba, D., Yelekci, K., Mitchell, J.B.O. Drug design for CNS diseases: Polypharmacological profiling of compounds using cheminformatic, 3D-QSAR and virtual screening methodologies (2016) Frontiers in Neuroscience, 10 (JUN), art. no. 265.

Nembri, S., Grisoni, F., Consonni, V., Todeschini, R. In Silico prediction of cytochrome P450-drug interaction: QSARs for CYP3a4 and CYP2C9 (2016) International Journal of Molecular Sciences, 17 (6), art. no. 914.

Jorge, E.G., Rayar, A.M., Barigye, S.J., Rodríguez, M.E.J., Veitía, M.S.-I. Development of an in silico model of DPPH free radical scavenging capacity: Prediction of antioxidant activity of coumarin type compounds (2016) International Journal of Molecular Sciences, 17 (6), art. no. 881

Sosnowska, A., Barycki, M., Gajewicz, A., Bobrowski, M., Freza, S., Skurski, P., Uhl, S., Laux, E., Journot, T., Jeandupeux, L., Keppner, H., Puzyn, T. Towards the Application of Structure–Property Relationship Modeling in Materials Science: Predicting the Seebeck Coefficient for Ionic Liquid/Redox Couple Systems (2016) ChemPhysChem, pp. 1591-1600.

Yu, X., Chen, J., Liu, W. Structure-property relationship of glass transition temperatures and motion units for polymethacrylates (2016) Gaofenzi Cailiao Kexue Yu Gongcheng/Polymeric Materials Science and Engineering, 32 (6), pp. 49-53.

Goodarzi, M., dos Santos Coelho, L., Honarparvar, B., Ortiz, E.V., Duchowicz, P.R. Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides (2016) Ecotoxicology and Environmental Safety, 128, pp. 52-60.

Sharma, N., Dwivedi, A., Srivastava, A.K., Singh, A. QSAR modeling of HIV-1 reverse transcriptase inhibitor of aryluracil derivatives using ab initio and empirical calculations (2016) Indian Journal of Chemistry - Section B Organic and Medicinal Chemistry, 55B (6), pp. 752-760.

Birck, M.G., Campos, L.J., De Melo, E.B. Computacional study of 1H-imidazol-2-yl-pyrimidine-4,6-diamines for identification of potential parent compounds of new antimalarial agents [Estudo computacional de 1H-imidazol-2-il-pirimidina-4,6-diaminas para a identificação de potenciais precursores de novos agentes antimaláricos] (2016) Quimica Nova, 39 (5), pp. 567-574.

Beiknejad, D., Chaichi, M.J., Fatemi, M.H. Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR) (2016) Journal of Physical Organic Chemistry, 29 (6), pp. 312-320.

Stavrou, M., Bardow, A., Gross, J. Estimation of the binary interaction parameter kij of the PC-SAFT Equation of State based on pure component parameters using a QSPR method (2016) Fluid Phase Equilibria, 416, pp. 138-149.

Beheshti, A., Pourbasheer, E., Nekoei, M., Vahdani, S. QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm-multiple linear regressions (2016) Journal of Saudi Chemical Society, 20 (3), pp. 282-290.

Fan, D., Liu, J., Wang, L., Yang, X., Zhang, S., Zhang, Y., Shi, L. Development of Quantitative Structure-Activity Relationship Models for Predicting Chronic Toxicity of Substituted Benzenes to Daphnia Magna (2016) Bulletin of Environmental Contamination and Toxicology, 96 (5), pp. 664-670.

Wisniewska, A., Lipinski, P.F.J., Wozniak, K., Sanjuan-Szklarz, F.W., Cieniecka-Roslonkiewicz, A., Michalczyk, A., Dabrowski, Z., Kulig-Adamiak, A., Matalinska, J., Les, A., Cybulski, J. Synthesis and antimicrobial properties of new mandelate ionic liquids (2016) Acta Poloniae Pharmaceutica - Drug Research, 73 (3), pp. 705-715.

Yilmaz, H., Ahmed, L., Rasulev, B., Leszczynski, J. Application of ligand- and receptor-based approaches for prediction of the HIV-RT inhibitory activity of fullerene derivatives (2016) Journal of Nanoparticle Research, 18 (5), art. no. 123.

Venkatraman, V., Alsberg, B.K. KRAKENX: software for the generation of alignment-independent 3D descriptors (2016) Journal of Molecular Modeling, 22 (4), art. no. 93.

Fu, Z., Chen, J., Li, X., Wang, Y., Yu, H. Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds (2016) Chemosphere, 148, pp. 118-125.

Halder, A.K., Mukherjee, A., Adhikari, N., Saha, A., Jha, T. Nitric oxide synthase (NOS) inhibitors in cancer angiogenesis (2016) Current Enzyme Inhibition, 12 (1), pp. 49-66.

Tomal, J.H., Welch, W.J., Zamar, R.H. Exploiting Multiple Descriptor Sets in QSAR Studies (2016) Journal of Chemical Information and Modeling, 56 (3), pp. 501-509.

Fourches, D., Pu, D., Li, L., Zhou, H., Mu, Q., Su, G., Yan, B., Tropsha, A. Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles (2016) Nanotoxicology, 10 (3), pp. 374-383.

Grisoni, F., Consonni, V., Vighi, M., Villa, S., Todeschini, R. Investigating the mechanisms of bioconcentration through QSAR classification trees (2016) Environment International, 88, pp. 198-205.

Sizochenko, N., Kuz’min, V., Ognichenko, L., Leszczynski, J. Introduction of simplex-informational descriptors for QSPR analysis of fullerene derivatives (2016) Journal of Mathematical Chemistry, 54 (3), pp. 698-706.

Yu, X., Huang, X. Prediction of glass transition temperatures of polyacrylates from the structures of motion units (2016) Journal of Theoretical and Computational Chemistry, 15 (2), art. no. 1650011.

Watkins, M., Sizochenko, N., Rasulev, B., Leszczynski, J. Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach (2016) Journal of Molecular Modeling, 22 (3), art. no. 55, pp. 1-14.

Martin, Y.C., Abagyan, R., Ferenczy, G.G., Gillet, V.J., Oprea, T.I., Ulander, J., Winkler, D., Zefirov, N.S. Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015) (2016) Pure and Applied Chemistry, 88 (3), pp. 239-264.

García-Jacas, C.R., Contreras-Torres, E., Marrero-Ponce, Y., Pupo-Meriño, M., Barigye, S.J., Cabrera-Leyva, L. Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets (2016) Journal of Cheminformatics, 8 (1), art. no. 122.

Gebreyohannes, S., Dadmohammadi, Y., Neely, B.J., Gasem, K.A.M. A Comparative Study of QSPR Generalized Activity Coefficient Model Parameters for Vapor-Liquid Equilibrium Mixtures (2016) Industrial and Engineering Chemistry Research, 55 (4), pp. 1102-1116.

Rybinska, A., Sosnowska, A., Barycki, M., Puzyn, T. Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids (2016) Journal of Computer-Aided Molecular Design, 30 (2), pp. 165-176.

Algamal, Z.Y., Lee, M.H., Al-Fakih, A.M. High-dimensional quantitative structure-activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression (2016) Journal of Chemometrics, 30 (2), pp. 50-57.

Guan, M., Su, G., Giesy, J.P., Zhang, X. Classification and toxicity mechanisms of novel flame retardants (NFRs) based on whole genome expression profiling (2016) Chemosphere, 144, pp. 2150-2157.

Dehmer, M., Sivakumar, L. On Comparability Graphs: Theory and Applications (2016) Advances in Mathematical Chemistry and Applications: Revised Edition, 1, pp. 139-160.

Rafiei, H., Khanzadeh, M., Mozaffari, S., Bostanifar, M.H., Avval, Z.M., Aalizadeh, R., Pourbasheer, E. Qsar study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR) (2016) EXCLI Journal, 15, pp. 38-53.

Yousefinejad, S., Honarasa, F., Solhjoo, A. On the Solubility of Ferrocene in Nonaqueous Solvents (2016) Journal of Chemical and Engineering Data, 61 (1), pp. 614-621.

Takieddin, K., Khimyak, Y.Z., Fábián, L. Prediction of Hydrate and Solvate Formation Using Statistical Models (2016) Crystal Growth and Design, 16 (1), pp. 70-81.

Al-Fakih, A.M., Aziz, M., Abdallah, H.H., Maarof, H., Jamaludin, R., Usman, B. Corrosion inhibition of Q235A steel in acid medium using isatin derivatives: A QSAR study [Perencatan kakisan keluli Q235A di dalam medium asid menggunakan terbitan isatin: Satu kajian QSAR] (2016) Malaysian Journal of Analytical Sciences, 20 (3), pp. 484-490.

Tarko, L. Influence of calibration and validation sets' similarity on the result of external validation test (2016) Match, 75 (3), pp. 511-532.

Garkani-Nejad, Z., Ghanbari, A. Application of support vector machine in QSAR study of triazolyl thiophenes as cyclin dependent kinase-5 inhibitors for their anti-alzheimer activity (2016) Indian Journal of Chemical Technology, 23 (1), pp. 9-21.

Ambure, P., Roy, K. Understanding the structural requirements of cyclic sulfone hydroxyethylamines as hBACE1 inhibitors against Aß plaques in Alzheimer's disease: A predictive QSAR approach (2016) RSC Advances, 6 (34), pp. 28171-28186.

Pareek, T., Bajaj, A.V., Mandloi, D. Modeling of Sulmazole analogues as cardiotonic agents (2016) Der Pharma Chemica, 8 (6), pp. 57-65.

Yousefinejad, S., Honarasa, F., Chaabi, M. New relationship models for solvent-pyrene solubility based on molecular structure and empirical properties (2016) New Journal of Chemistry, 40 (12), pp. 10197-10207.

Tarko, L., Hirtopeanu, A. QSAR study regarding the inhibitory activity of some iminosugars against a-glucosidase (2016) Revista de Chimie, 67 (1), pp. 13-16.

Das, R.N., Sintra, T.E., Coutinho, J.A.P., Ventura, S.P.M., Roy, K., Popelier, P.L.A. Development of predictive QSAR models for: Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests (2016) Toxicology Research, 5 (5), pp. 1388-1399.

Hassanzadeh, Z., Ghavami, R., Kompany-Zareh, M. Radial basis function neural networks based on the projection pursuit and principal component analysis approaches: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors (2016) Medicinal Chemistry Research, 25 (1), pp. 19-29.

Knight, N.J., Hernando, E., Haynes, C.J.E., Busschaert, N., Clarke, H.J., Takimoto, K., García-Valverde, M., Frey, J.G., Quesada, R., Gale, P.A. QSAR analysis of substituent effects on tambjamine anion transporters (2016) Chemical Science, 7 (2), pp. 1600-1608.

Simeon, S., Anuwongcharoen, N., Shoombuatong, W., Malik, A.A., Prachayasittikul, V., Wikberg, J.E.S., Nantasenamat, C. Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking (2016) PeerJ, 2016 (9), art. no. e2322.

Prado-Prado, F.J., Arguello-Chan, A.G., Estrada-Domínguez, C.I., Aguirre-Crespo, A., Aguirre-Crespo, F.J., García-Mera, X. Model for classification of a ChEMBL dataset of anti-breast cancer-compounds conforming to multiple pharmacological parameters, biological assays, and receptors (2016) Letters in Drug Design and Discovery, 13, pp. 491-496.

Nekoeinia, M., Yousefinejad, S., Abdollahi-Dezaki, A. Prediction of ETN Polarity Scale of Ionic Liquids Using a QSPR Approach (2015) Industrial and Engineering Chemistry Research, 54 (50), pp. 12682-12689.

Yu, H.-X., Li, D.-X., Xu, F., Chai, P., Pan, Q., Wang, D.-D. Quantitative structure-activity relationship during the freezing process of cryoprotectants used for thermal expansion of articular cartilage (2015) Modern Food Science and Technology, 31 (12).

Yousefinejad, S., Hemmateenejad, B. Chemometrics tools in QSAR/QSPR studies: A historical perspective (2015) Chemometrics and Intelligent Laboratory Systems, 149, pp. 177-204.

Kovalishyn, V., Poda, G. Efficient variable selection batch pruning algorithm for artificial neural networks (2015) Chemometrics and Intelligent Laboratory Systems, 149, pp. 10-16.

Dong, J., Cao, D.-S., Miao, H.-Y., Liu, S., Deng, B.-C., Yun, Y.-H., Wang, N.-N., Lu, A.-P., Zeng, W.-B., Chen, A.F. ChemDes: An integrated web-based platform for molecular descriptor and fingerprint computation (2015) Journal of Cheminformatics, 7 (1), art. no. 60.

Batra, A., Nandi, S., Bagchi, M.C. QSAR and pharmacophore modeling of indole-based C-3 pyridone compounds as HCV NS5B polymerase inhibitors utilizing computed molecular descriptors (2015) Medicinal Chemistry Research, 24 (6), pp. 2432-2440.

Chen, B., Zhang, T., Bond, T., Gan, Y. Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources (2015) Journal of Hazardous Materials, 299, pp. 260-279.

Parihar, N., Nandi, S. In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors (2015) SpringerPlus, 4 (1), art. no. 819, pp. 1-20.

Pourbasheer, E., Aalizadeh, R., Shiri, H.M., Banaei, A., Ganjali, M.R. 2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA (2015) Current Computer-Aided Drug Design, 11 (4), pp. 292-303.

Duardo-Sanchez, A., González-Díaz, H., Pazos, A. MI-NODES multiscale models of metabolic reactions, brain connectome, ecological, epidemic, world trade, and legal-social networks (2015) Current Bioinformatics, 10 (5), pp. 692-713.

Munteanu, C.R., Aguiar-Pulido, V., Freire, A., Martínez-Romero, M., Porto-Pazos, A.B., Pereira, J., Dorado, J. Graph-based processing of macromolecular information (2015) Current Bioinformatics, 10 (5), pp. 606-631.

Worachartcheewan, A., Prachayasittikul, V., Toropova, A.P., Toropov, A.A., Nantasenamat, C. Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors (2015) Molecular Diversity, 19 (4), pp. 955-964.

Momeni, M., Riahi, S. An investigation into the relationship between molecular structure and rich/lean loading of linear amine-based CO<inf>2</inf> absorbents (2015) International Journal of Greenhouse Gas Control, 42, pp. 157-164.

Pérez-Garrido, A., Rivero-Buceta, V., Cano, G., Kumar, S., Pérez-Sánchez, H., Bautista, M.T. Latest QSAR study of adenosine A2B receptor affinity of xanthines and deazaxanthines (2015) Molecular Diversity, 19 (4), pp. 975-989.

Zhou, L.L., Jiang, J.C., Pan, Y., Wang, Z.R. A mathematical method for predicting heat of reaction of organic peroxides (2015) Journal of Loss Prevention in the Process Industries, 38, pp. 254-259.

Wang, Y., Bai, F., Cao, H., Li, J., Liu, H., Gramatica, P. A combined quantitative structure-Activity relationship research of quinolinone derivatives as androgen receptor antagonists (2015) Combinatorial Chemistry and High Throughput Screening, 18 (9), pp. 834-845.

Ghasemi, J.B., Nazarshodeh, E., Abedi, H. Molecular docking, 2D and 3D-QSAR studies of new indole-based derivatives as HCV-NS5B polymerase inhibitors (2015) Journal of the Iranian Chemical Society, 12 (10), pp. 1789-1799.

Castillo-González, D., Mergny, J.-L., De Rache, A., Pérez-Machado, G., Cabrera-Pérez, M.A., Nicolotti, O., Introcaso, A., Mangiatordi, G.F., Guédin, A., Bourdoncle, A., Garrigues, T., Pallardó, F., Cordeiro, M.N.D.S., Paz-Y-Miño, C., Tejera, E., Borges, F., Cruz-Monteagudo, M. Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands (2015) Journal of Chemical Information and Modeling, 55 (10), pp. 2094-2110.

Pingaew, R., Prachayasittikul, V., Worachartcheewan, A., Nantasenamat, C., Prachayasittikul, S., Ruchirawat, S., Prachayasittikul, V. Novel 1,4-naphthoquinone-based sulfonamides: Synthesis, QSAR, anticancer and antimalarial studies (2015) European Journal of Medicinal Chemistry, 103, art. no. 8098, pp. 446-459.

Cubillán, N., Marrero-Ponce, Y., Ariza-Rico, H., Barigye, S.J., García-Jacas, C.R., Valdes-Martini, J.R., Alvarado, Y.J. Novel global and local 3D atom-based linear descriptors of the Minkowski distance matrix: theory, diversity–variability analysis and QSPR applications (2015) Journal of Mathematical Chemistry, 53 (9), pp. 2028-2064.

Wenz, J.J. Molecular properties of steroids involved in their effects on the biophysical state of membranes (2015) Biochimica et Biophysica Acta - Biomembranes, 1848 (10), art. no. 81961,

Barigye, S.J., Freitas, M.P. Is molecular alignment an indispensable requirement in the MIA-QSAR method? (2015) Journal of Computational Chemistry, 36 (23), pp. 1748-1755.

Doosti, E., Shahlaei, M. QSAR analysis of some inhibitors for p38 map kinase using combination of principal component analysis and artificial intelligence (2015) Combinatorial Chemistry and High Throughput Screening, 18 (8), pp. 767-783.

Luo, M., Reid, T.-E., Wang, X.S. Discovery of natural product-derived 5-HT1A receptor binders by cheminfomatics modeling of known binders, high throughput screening and experimental validation (2015) Combinatorial Chemistry and High Throughput Screening, 18 (7), pp. 685-692.

Araujo, S.C., Maltarollo, V.G., Silva, D.C., Gertrudes, J.C., Honorio, K.M. ALK-5 inhibition: A molecular interpretation of the main physicochemical properties related to bioactive ligands (2015) Journal of the Brazilian Chemical Society, 26 (9), pp. 1936-1946.

Karthikeyan, M., Pandit, D., Vyas, R. Chem screener: A distributed computing tool for scaffold based virtual screening (2015) Combinatorial Chemistry and High Throughput Screening, 18 (6), pp. 544-561.

Aalizadeh, R., Pourbasheer, E., Ganjali, M.R. Analysis of B-RafV600Einhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies (2015) Molecular Diversity, 19 (4), pp. 915-930.

Pourbasheer, E., Vahdani, S., Aalizadeh, R., Banaei, A., Ganjali, M.R. QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions (2015) Journal of Chemical Sciences, 127 (7), pp. 1243-1251.

Maldonado-Rojas, W., Olivero-Verbel, J., Marrero-Ponce, Y. Computational fishing of new DNA methyltransferase inhibitors from natural products (2015) Journal of Molecular Graphics and Modelling, 60, pp. 43-54.

Zakariazadeh, M., Barzegar, A., Soltani, S., Aryapour, H. Developing 2D-QSAR models for naphthyridine derivatives against HIV-1 integrase activity (2015) Medicinal Chemistry Research, 24 (6), pp. 2485-2504.

Raevsky, O.A., Polianczyk, D.E., Grigorev, V.Y., Raevskaja, O.E., Dearden, J.C. In silico Prediction of Aqueous Solubility: A Comparative Study of Local and Global Predictive Models (2015) Molecular Informatics, 34 (6-7), pp. 417-430.

Srungboonmee, K., Songtawee, N., Monnor, T., Prachayasittikul, V., Nantasenamat, C. Probing the origins of 17ß-hydroxysteroid dehydrogenase type 1 inhibitory activity via QSAR and molecular docking (2015) European Journal of Medicinal Chemistry, 96, pp. 231-237.

Castillo-Garit, J.A., Del Toro-Cortés, O., Vega, M.C., Rolón, M., Rojas De Arias, A., Casañola-Martin, G.M., Escario, J.A., Gómez-Barrio, A., Marrero-Ponce, Y., Torrens, F., Abad, C. Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening (2015) European Journal of Medicinal Chemistry, 96, pp. 238-244.

Yilmaz, H., Rasulev, B., Leszczynski, J. Modeling the dispersibility of single walled carbon nanotubes in organic solvents by quantitative structure-activity relationship approach (2015) Nanomaterials, 5 (2), pp. 778-791.

Ma, S., Lv, M., Zhang, X., Zhai, H., Lv, W. Computational study of the effects of cations and anions to the cytotoxicity of diverse ionic liquids by supervised machine learning (2015) Chemometrics and Intelligent Laboratory Systems, 144, pp. 138-147.

Gupta, S., Basant, N., Singh, K.P. Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose (2015) Ecotoxicology, 24 (4), pp. 873-886.

D'Archivio, A.A., Maggi, M.A., Ruggieri, F. Quantitative structure-retention relationships of cannabimimetic aminoalkilindole derivatives and their metabolites (2015) Journal of Pharmaceutical and Biomedical Analysis, 109, pp. 136-141.

Urias, R.W.P., Barigye, S.J., Marrero-Ponce, Y., García-Jacas, C.R., Valdes-Martiní, J.R., Perez-Gimenez, F. IMMAN: free software for information theory-based chemometric analysis (2015) Molecular Diversity, 19 (2), pp. 305-319.

Khatri, N., Dutt, R., Madan, A.K. Role of moving average analysis for development of multi-target (Q)SAR models (2015) Mini-Reviews in Medicinal Chemistry, 15 (8), pp. 659-676.

Zhang, Y.-H., Xia, Z.-N., Yan, L., Liu, S.-S. Prediction of placental barrier permeability: A model based on partial least squares variable selection procedure (2015) Molecules, 20 (5), pp. 8270-8286.

Andrada, M.F., Vega-Hissi, E.G., Estrada, M.R., Garro Martinez, J.C. Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors (2015) Chemometrics and Intelligent Laboratory Systems, 143, pp. 122-129.

Asadollahi-Baboli, M., Mani-Varnosfaderani, A. Therapeutic index modeling and predictive QSAR of novel thiazolidin-4-one analogs against Toxoplasma gondii (2015) European Journal of Pharmaceutical Sciences, 70, pp. 117-124.

Kiani-Anbouhi, R., Ganjali, M.R., Norouzi, P. Application of QSPR for prediction of the complexation stabilities of Sm(III) with ionophores applied in lanthanoid sensors (2015) Journal of Inclusion Phenomena and Macrocyclic Chemistry, 81 (3-4), pp. 441-450.

Liu, Y., Winkler, D.A., Epa, V.C., Zhang, B., Yan, B. Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries (2015) Nano Research, 8 (4), pp. 1293-1308.

Venkatraman, V., Abburu, S., Alsberg, B.K. Can chemometrics be used to guide the selection of suitable DFT functionals? (2015) Chemometrics and Intelligent Laboratory Systems, 142, pp. 87-94.

Judycka-Proma, U., Bober, L., Gajewicz, A., Puzyn, T., Blazejowski, J. Chemometric analysis of correlations between electronic absorption characteristics and structural and/or physicochemical parameters for ampholytic substances of biological and pharmaceutical relevance (2015) Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 138, pp. 700-710.

Roy, K., Kar, S., Das, R.N. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment (2015) Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, pp. 1-479.

Fatemi, M.H., Heidari, A., Gharaghani, S. QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors (2015) Journal of Theoretical Biology, 369, pp. 13-22.

Noorizadeh, H., Farmany, A. Quantitative structure-electrochemistry relationship for substituted benzenoids using Levenberg-Marquardt artificial neural network (2015) Russian Journal of Electrochemistry, 51 (3), pp. 249-257.

Valadkhani, A., Asadollahi-Baboli, M., Mani-Varnosfaderani, A. QSAR study of the inhibitors of the acetyl-CoA carboxylase 1 and 2 using Bayesian regularized genetic neural networks: A comparative study (2015) Journal of the Brazilian Chemical Society, 26 (3), pp. 619-631.

Vucicevic, J., Nikolic, K., Dobricic, V., Agbaba, D. Prediction of blood-brain barrier permeation of a-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis (2015) European Journal of Pharmaceutical Sciences, 68, pp. 94-105.

Mracec, M., Mracec, M. QSAR study of 4-hydroxybiphenyl derivatives active on ERß subtype (2015) Revue Roumaine de Chimie, 60 (2-3), pp. 151-160.

Wenlock, M.C., Carlsson, L.A. How experimental errors influence drug metabolism and pharmacokinetic QSAR/QSPR models (2015) Journal of Chemical Information and Modeling, 55 (1), pp. 125-134.

D'Archivio, A.A., Maggi, M.A., Ruggieri, F. Artificial neural network prediction of multilinear gradient retention in reversed-phase HPLC: comprehensive QSRR-based models combining categorical or structural solute descriptors and gradient profile parameters (2015) Analytical and Bioanalytical Chemistry, 407 (4), art. no. 8317, pp. 1181-1190.

Rojas, C., Duchowicz, P.R., Tripaldi, P., Diez, R.P. QSPR analysis for the retention index of flavors and fragrances on a OV-101 column (2015) Chemometrics and Intelligent Laboratory Systems, 140, pp. 126-132.

Golmohammadi, H., Dashtbozorgi, Z., Samani, M.G., Acree, W.E. QSPR prediction of gas-to-methanol solvation enthalpy of organic compounds using replacement method and support vector machine (2015) Physics and Chemistry of Liquids, 53 (1), pp. 46-66.

Al-Fakih, A.M., Aziz, M., Abdallah, H.H., Algamal, Z.Y., Lee, M.H., Maarof, H. High dimensional QSAR study of mild steel corrosion inhibition in acidic medium by furan derivatives (2015) International Journal of Electrochemical Science, 10 (4), pp. 3568-3583.

Basak, S.C., Majumdar, S. Prediction of mutagenicity of chemicals from their calculated molecular descriptors: A case study with structurally homogeneous versus diverse datasets (2015) Current Computer-Aided Drug Design, 11 (2), pp. 117-123.

Nandi, S., Bagchi, M.C. QSAR of chalcones utilizing theoretical molecular descriptors (2015) Current Computer-Aided Drug Design, 11 (2), pp. 184-193.

Singh, S. Computational design and chemometric QSAR modeling of Plasmodium falciparum carbonic anhydrase inhibitors (2015) Bioorganic and Medicinal Chemistry Letters, 25 (1), pp. 133-141.

García-Jacas, C.R., Aguilera-Mendoza, L., González-Pérez, R., Marrero-Ponce, Y., Acevedo-Martínez, L., Barigye, S.J., Avdeenko, T. Multi-server approach for high-throughput molecular descriptors calculation based on multi-linear algebraic maps (2015) Molecular Informatics, 34 (1), pp. 60-69.

Speck-Planche, A., Cordeiro, M.N.D.S. Multi-target QSAR approaches for modeling protein inhibitors. Simultaneous prediction of activities against biomacromolecules present in Gram-negative bacteria (2015) Current Topics in Medicinal Chemistry, 15 (18), pp. 1801-1813.

Zhang, C., Cheng, F., Sun, L., Zhuang, S., Li, W., Liu, G., Lee, P.W., Tang, Y. In silico prediction of chemical toxicity on avian species using chemical category approaches (2015) Chemosphere, 122, pp. 280-287.

Tao, L., Zhang, P., Qin, C., Chen, S.Y., Zhang, C., Chen, Z., Zhu, F., Yang, S.Y., Wei, Y.Q., Chen, Y.Z. Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools (2015) Advanced Drug Delivery Reviews, 86, pp. 83-100.

Cao, D.-S., Xiao, N., Xu, Q.-S., Chen, A.F. Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions (2015) Bioinformatics, 31 (2), pp. 279-281.

Pirovano, A., Brandmaier, S., Huijbregts, M.A.J., Ragas, A.M.J., Veltman, K., Hendriks, A.J. The utilisation of structural descriptors to predict metabolic constants of xenobiotics in mammals (2015) Environmental Toxicology and Pharmacology, 39 (1), pp. 247-258.

MontaÑez-GodÍnez, N., MartÍnez-OlguÍn, A.C., Deeb, O., GarduÑo-JuÁrez, R., RamÍrez-Galicia, G. Qsar/qspr as an application of artifi cial neural networks (2015) Methods in Molecular Biology, 1260, pp. 319-333.

Lambrinidis, G., Vallianatou, T., Tsantili-Kakoulidou, A. In vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review (2015) Advanced Drug Delivery Reviews, 86, pp. 27-45.

Scior, T., Lozano-Aponte, J., Ajmani, S., Hernández-Montero, E., Chávez-Silva, F., Hernández-Núñez, E., Moo-Puc, R., Fraguela-Collar, A., Navarrete-Vázquez, G. Antiprotozoal nitazoxanide derivatives: Synthesis, bioassays and QSAR study combined with docking for mechanistic insight (2015) Current Computer-Aided Drug Design, 11 (1), pp. 21-31.

Ambure, P., Roy, K. Exploring structural requirements of imaging agents against Aß plaques in Alzheimer's disease: A QSAR approach (2015) Combinatorial Chemistry and High Throughput Screening, 18 (4), pp. 411-419.

Kritikos, N., Vallianatou, T., Dotsikas, Y., Tsantili-Kakoulidou, A. Chemometrics-chem(O)informatics as a tool in quantitative structure – Property and structure – Activity relationships, qspr & qsar (2015) Pharmakeftiki, 27 (3), pp. 73-100.

Dashtbozorgi, Z., Golmohammadi, H., Acree, W.E. Prediction of gas-to-ionic liquid partition coefficient of organic solutes dissolved in 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate using QSPR approaches (2015) Journal of Molecular Liquids, 201, pp. 21-29.

Basak, S.C., Majumdar, S. The importance of rigorous statistical practice in the current landscape of QSAR modelling (2015) Current Computer-Aided Drug Design, 11 (1), pp. 2-4.

Venkatraman, V., Alsberg, B.K. A quantitative structure-property relationship study of the photovoltaic performance of phenothiazine dyes (2015) Dyes and Pigments, 114 (C), pp. 69-77.

Nandy, A., Roy, K., Saha, A. Exploring molecular fingerprints of selective PPARd agonists through comparative and validated chemometric techniques (2015) SAR and QSAR in Environmental Research, 26 (5), pp. 363-382.

Aher, R.B., Roy, K. Understanding the structural requirements in diverse scaffolds for the inhibition of P. falciparum dihydroorotate dehydrogenase (PfDHODH) using 2D-QSAR, 3D-pharmacophore and structure-based energy-optimized pharmacophore models (2015) Combinatorial Chemistry and High Throughput Screening, 18 (2), pp. 217-226.

Ojha, P.K., Roy, K. The current status of Antimalarial drug research with special reference to application of QSAR models (2015) Combinatorial Chemistry and High Throughput Screening, 18 (2), pp. 91-128.

Mansourian, M., Fassihi, A., Saghaie, L., Madadkar-Sobhani, A., Mahnam, K., Abbasi, M. QSAR and docking analysis of A2B adenosine receptor antagonists based on non-xanthine scaffold (2015) Medicinal Chemistry Research, 24 (1), pp. 394-407.

Choudhary, M., Sharma, B.K. QSAR rationales for the isoindolone derivatives as 5-HT<inf>2C</inf> receptor antagonists (2015) Research Journal of Pharmaceutical, Biological and Chemical Sciences, 6 (3), pp. 1725-1736.

Beteringhe, A., Radutiu, A.C., Mischie, A., Spafiu, F. A new method to define the hydrophilic-lipophilic balance (2015) Journal of Optoelectronics and Advanced Materials, 17 (5-6), pp. 846-855.

Halder, A.K., Saha, A., Saha, K.D., Jha, T. Stepwise development of structure-activity relationship of diverse PARP-1 inhibitors through comparative and validated in silico modeling techniques and molecular dynamics simulation (2015) Journal of Biomolecular Structure and Dynamics, 33 (8), pp. 1756-1779.

Fu, L., Li, J.J., Wang, Y., Wang, X.H., Wen, Y., Qin, W.C., Su, L.M., Zhao, Y.H. Evaluation of toxicity data to green algae and relationship with hydrophobicity (2015) Chemosphere, 120, pp. 16-22.

Wang, J., Hou, T. Advances in computationally modeling human oral bioavailability (2015) Advanced Drug Delivery Reviews, 86, pp. 11-16.

Kar, S., Roy, K. Predictive toxicity modelling of benzodiazepine drugs using multiple in silico approaches: Descriptor-based QSTR, group-based QSTR and 3D-toxicophore mapping (2015) Molecular Simulation, 41 (4), pp. 345-355.

Yu, X., Shi, D., Zhi, X., Li, Q., Yao, X., Xu, H. Synthesis and quantitative structure-activity relationship (QSAR) study of C7-oxime ester derivatives of obacunone as insecticidal agents (2015) RSC Advances, 5 (40), pp. 31700-31707.

Choudhary, M., Pilania, P., Sharma, B.K. QSAR rationales for the 5-HT2A receptor antagonistic activity of 2-alkyl-4-aryl-pyrimidine fused heterocycles (2015) Research Journal of Pharmaceutical, Biological and Chemical Sciences, 6 (2), pp. 326-338.

Das, R.N., Roy, K., Popelier, P.L.A. Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines (2015) Chemosphere, 139, pp. 163-173.

Dobrowolski, J.C. The chiral graph theory (2015) Match, 73 (2), pp. 347-374.

Martincic, R., Kuzmanovski, I., Wagner, A., Novic, M. Development of models for prediction of the antioxidant activity of derivatives of natural compounds (2015) Analytica Chimica Acta, 868, pp. 23-35.

Martin, T.M., Young, D.M., Lilavois, C.R., Barron, M.G. Comparison of global and mode of action-based models for aquatic toxicity (2015) SAR and QSAR in Environmental Research, 26 (3), pp. 245-262.

Ahmadi, M., Shahlaei, M. Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods (2015) Research in Pharmaceutical Sciences, 10 (4), pp. 307-325.

Tantra, R., Oksel, C., Puzyn, T., Wang, J., Robinson, K.N., Wang, X.Z., Ma, C.Y., Wilkins, T. Nano(Q)SAR: Challenges, pitfalls and perspectives (2015) Nanotoxicology, 9 (5), pp. 636-642.









Sawada, R., Kotera, M., Yamanishi, Y. Benchmarking a wide range of chemical descriptors for drug-target interaction prediction using a chemogenomic approach (2014) Molecular Informatics, 33 (11-12), pp. 719-731.

Thirugnanasambandham, K., Sivakumar, V., Prakash Maran, J. Process optimization and analysis of microwave assisted extraction of pectin from dragon fruit peel (2014) Carbohydrate Polymers, 112, pp. 622-626.

Chavan, S., Nicholls, I.A., Karlsson, B.C.G., Rosengren, A.M., Ballabio, D., Consonni, V., Todeschini, R. Towards global QSAR model building for acute toxicity: Munro database case study (2014) International Journal of Molecular Sciences, 15 (10), pp. 18162-18174.

Lopes-Costa, T., Basílio, N., Pedrosa, J.M., Pina, F. Photochromism of the natural dye 7,4'-dihydroxy-5-methoxyflavylium (dracoflavylium) in the presence of (2-hydroxypropyl)-ß-cyclodextrin (2014) Photochemical and Photobiological Sciences, 13 (10), pp. 1420-1426.

Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Prachayasittikul, V. Probing the origins of anticancer activity of chrysin derivatives (2014) Medicinal Chemistry Research, 9 p. Article in Press.

Iman, M., Davood, A., Banarouei, N. QSAR study of chalcone derivatives as anti-Leishmania agents (2014) Turkish Journal of Chemistry, 38 (5), pp. 716-724.

Hara, K., Zheng, G., Qu, Q., Liu, H., Ouyang, Z., Chen, Z., Tomchick, D.R., Yu, H. Structure of cohesin subcomplex pinpoints direct shugoshin-Wapl antagonism in centromeric cohesion (2014) Nature Structural and Molecular Biology, . Article in Press.

Alencar Filho, E.B., Weber, K.C., Vasconcellos, M.L.A.A. Selection of 2D/3D molecular descriptors and QSAR modeling of aromatic Morita-Baylis-Hillman adducts with leishmanicidal activities (2014) Medicinal Chemistry Research, . Article in Press.

Tugcu, G., Yilmaz, H.B., Türker Saçan, M. Comparative performance of descriptors in a multiple linear and Kriging models: a case study on the acute toxicity of organic chemicals to algae (2014) Environmental Science and Pollution Research, . Article in Press.

Yarbrough, J., Greenacre, C., Cox, S. Determination of butorphanol using high performance liquid chromatography in small volume plasma samples (2014) Journal of Liquid Chromatography and Related Technologies, 37 (9), pp. 1270-1277.

Ghandadi, M., Shayanfar, A., Hamzeh-Mivehroud, M., Jouyban, A. Quantitative structure activity relationship and docking studies of imidazole-based derivatives as P-glycoprotein inhibitors (2014) Medicinal Chemistry Research, . Article in Press.

Mei, L., Wu, Q.-Y., Liu, C.-M., Zhao, Y.-L., Chai, Z.-F., Shi, W.-Q. The first case of an actinide polyrotaxane incorporating cucurbituril: A unique 'dragon-like' twist induced by a specific coordination pattern of uranium (2014) Chemical Communications, 50 (27), pp. 3612-3615.

Khatri, N., Madan, A.K. Models for H3 receptor antagonist activity of sulfonylurea derivatives (2014) Journal of Molecular Graphics and Modelling, 48, pp. 87-95.

Ahmadi, S., Khazaei, M.R., Abdolmaleki, A. Quantitative structure-property relationship study on the intercalation of anticancer drugs with ct-DNA (2014) Medicinal Chemistry Research, 23 (3), pp. 1148-1161.

Zhu, X.-W., Sedykh, A., Liu, S.-S. Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information (2014) Journal of Applied Toxicology, 34 (3), pp. 281-288.

Lucic, B., Stepanic, V., Plavšic, D., Amic, A., Amic, D. Correlation between 13C NMR chemical shifts and antiradical activity of flavonoids (2014) Monatshefte fur Chemie, 145 (3), pp. 457-463.

Usman, B., Maarof, H., Abdallah, H.H., Jamaludin, R., Al-Fakih, A.M., Aziz, M. Corrosion inhibition efficiency of thiophene derivatives on mild steel: A QSAR model (2014) International Journal of Electrochemical Science, 9 (4), pp. 1678-1689.

Zhang, X.-L., Zhou, Z.-X., Liu, Y.-H., Fan, X.-L., Li, H.-D., Wang, J.-T. Predicting the acute toxicity of aromatic amines by linear and nonlinear regression methods (2014) Jiegou Huaxue, 33 (2), pp. 244-252.

Worachartcheewan, A., Nantasenamat, C., Owasirikul, W., Monnor, T., Naruepantawart, O., Janyapaisarn, S., Prachayasittikul, S., Prachayasittikul, V. Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression (2014) European Journal of Medicinal Chemistry, 73, pp. 258-264.

Amiri, M., Ajloo, D. QSAR and docking studies on the diaryltriazine analogs as HIV-1 reverse transcriptase inhibitors (2014) Medicinal Chemistry Research, 23 (2), pp. 969-979.

Sardari, S., Kohanzad, H., Ghavami, G. Artificial neural network modeling of antimycobacterial chemical space to introduce efficient descriptors employed for drug design (2014) Chemometrics and Intelligent Laboratory Systems, 130, pp. 151-158.

Liu, X., Liu, Y., Yao, B., Ma, Y., Lian, H. On odd-graceful labelings of irregular dragon graphs (2014) PIC 2014 - Proceedings of 2014 IEEE International Conference on Progress in Informatics and Computing, art. no. 6972368, pp. 415-418.

Hu, X.-J., Wang, X.-X., Liu, M.-S., Tai, Z.-G., Li, G.-F. Quantitative evaluation of loureirin A and loureirin B in dragon’s blood capsules from different manufacturers by HPLC (2014) Journal of Chemical and Pharmaceutical Research, 6 (7), pp. 2243-2248.

Fan, J.-Y., Yi, T., Sze-To, C.-M., Zhu, L., Peng, W.-L., Zhang, Y.-Z., Zhao, Z.-Z., Chen, H.-B. A systematic review of the botanical, phytochemical and pharmacological profile of dracaena cochinchinensis, a plant source of the ethnomedicine dragon's blood (2014) Molecules, 19 (7), pp. 10650-10669.

Su, X.-Q., Song, Y.-L., Zhang, J., Huo, H.-X., Huang, Z., Zheng, J., Zhang, Q., Zhao, Y.-F., Xiao, W., Li, J., Tu, P.-F. Dihydrochalcones and homoisoflavanes from the red resin of Dracaena cochinchinensis (Chinese dragon's blood) (2014) Fitoterapia, 99, pp. 64-71.

Molina, R., Clemente, E., Scapim, M.R.S., Vagula, J.M. Physical Evaluation and Hygroscopic Behavior of Dragon Fruit (Hylocereus undatus) Lyophilized Pulp Powder (2014) Drying Technology, 32 (16), pp. 2005-2011.

Martinez, J.C.G., Vega-Hissi, E.G., Andrada, M.F., Duchowicz, P.R., Torrens, F., Estrada, M.R. Lacosamide derivatives with anticonvulsant activity as carbonic anhydrase inhibitors. Molecular modeling, docking and QSAR analysis (2014) Current Computer-Aided Drug Design, 10 (2), pp. 160-167.

Van Opstal, B., Janin, L., Museth, K., Aldén, M. Large scale simulation and surfacing of water and ice effects in dragons 2 (2014) ACM SIGGRAPH 2014 Talks, SIGGRAPH 2014, art. no. 11, .

Sobrinho, J.L., Vanbever, L., Le, F., Rexford, J. Distributed route aggregation on the global network (2014) CoNEXT 2014 - Proceedings of the 2014 Conference on Emerging Networking Experiments and Technologies, pp. 161-172.

Wang, Y., Song, L. An algorithmic and systematic approach for improving robustness of TOA-based localization (2014) Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013, art. no. 6832180, pp. 2066-2073.

Zhang, X.-L., Zhou, Z.-X., Fan, X.-L., Liu, Y.-H., Li, H.-D., Wang, J.T. QSTR studies on acute toxicity and mutagenicity of halogenated benzenes (2014) Asian Journal of Chemistry, 26 (9), pp. 2707-2710.

Lyakurwa, F.S., Yang, X., Li, X., Qiao, X., Chen, J. Development of in silico models for predicting LSER molecular parameters and for acute toxicity prediction to fathead minnow (Pimephales promelas) (2014) Chemosphere, 108, pp. 17-25.

Alias, A.N., MatJafri, M.Z., Lim, H.S., Saleh, N.M., Chumiran, S.H., Mohamad, A. Inferring angstrom exponent and aerosol optical depth from aeronet (2014) Journal of Environmental Science and Technology, 7 (3), pp. 166-175.

Gautam, A., Chaudhary, K., Singh, S., Joshi, A., Anand, P., Tuknait, A., Mathur, D., Varshney, G.C., Raghava, G.P.S. Hemolytik: A database of experimentally determined hemolytic and non-hemolytic peptides (2014) Nucleic Acids Research, 42 (D1), pp. D444-D449.

Shi, J.-J., Chen, L.-P., Chen, W.-H. Qspr models of compound viscosity based on iterative self-organizing data analysis technique and ant colony algorithm (2014) Wuli Huaxue Xuebao/ Acta Physico - Chimica Sinica, 30 (5), pp. 803-810.

Stasiak, J., Koba, M., Baczek, T. Quantitative structure-retention relationships studies of selected groups of compounds characterized by different pharmacological activity using multiple linear regression procedure (2014) Letters in Drug Design and Discovery, 11 (8), pp. 1017-1039.

Samidurai, K., Mathew, N. Bioassay guided fractionation and GC-MS analysis of euphorbia lactea extract for mosquito larvicidal activity (2014) International Journal of Pharmacy and Pharmaceutical Sciences, 6 (4), pp. 344-347.

Fedyushkina, I.V., Romero Reyes, I.V., Lagunin, A.A., Skvortsov, V.S. Prediction of the action of ligands of steroid hormone receptors (2014) Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry, 8 (1), pp. 53-58.

Crisan, L., Pacureanu, L., Avram, S., Bora, A., Avram, S., Kurunczi, L. PLS and shape-based similarity analysis of maleimides-GSK-3 inhibitors (2014) Journal of Enzyme Inhibition and Medicinal Chemistry, 29 (4), pp. 599-610.

Zhang, L., Qu, W., Guo, Y., Li, S. Automatic abstraction for verification of parameterized systems (2014) Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 26 (6), pp. 991-998.

Zhang, X., Zhang, X., Li, Q., Sun, Z., Song, L., Sun, T. Support Vector Machine Applied to Study on Quantitative Structure–Retention Relationships of Polybrominated Diphenyl Ether Congeners (2014) Chromatographia, 77 (19-20), pp. 1387-1398.

Cheng, Y., Xue, J., Jiang, H., Wang, M., Gao, L., Ma, D., Zhang, Z. Neuroprotective effect of resveratrol on arsenic trioxide-induced oxidative stress in feline brain (2014) Human and Experimental Toxicology, 33 (7), pp. 737-747.

Hoste, L., Signer, B. Water Ball Z: An augmented fighting game using water as tactile feedback (2014) TEI 2014 - 8th International Conference on Tangible, Embedded and Embodied Interaction, Proceedings, pp. 173-176.

Billones, L.T., Billones, J.B. A univariate analysis of molecular properties and inhibitory activity of dihydrothiophenones against dihydroorotate dehydrogenase of malaria parasite (2014) Journal of Chemical and Pharmaceutical Research, 6 (8), pp. 209-217.

Corbari, C., Mancini, M., Su, Z., Li, J. Evapotranspiration estimate from water balance closure using satellite data for the Upper Yangtze River basin (2014) Hydrology Research, 45 (4-5), pp. 603-614.

Sachan, S., Tiwari, S., Tiwari, S.K. QSAR study of flavonoids analogues as in vivo anticancer BCRP inhibition bio-activity (2014) International Journal of PharmTech Research, 6 (5), pp. 1687-1694.

Li, C., Yang, X., Li, X., Chen, J., Qiao, X. Development of a model for predicting hydroxyl radical reaction rate constants of organic chemicals at different temperatures (2014) Chemosphere, 95, pp. 613-618.

Donadio, P., Fioccola, G.B., Canonico, R., Ventre, G. Network security for hybrid cloud (2014) 2014 Euro Med Telco Conference - From Network Infrastructures to Network Fabric: Revolution at the Edges, EMTC 2014, art. no. 6996640, .

Rusu, E. Evaluation of the wave energy conversion efficiency in various coastal environments (2014) Energies, 7 (6), pp. 4002-4018.

Uras, T., Koenig, S. Identifying hierarchies for fast optimal search (2014) Proceedings of the National Conference on Artificial Intelligence, 2, pp. 878-884.

Fang, G.-C., Chang, C.-Y., Tsai, J.-H., Lin, C.-C. The size distributions of ambient air metallic pollutants by using a multi-stage MOUDI sampler (2014) Aerosol and Air Quality Research, 14 (3), pp. 970-980.

Kousha, M., Farhadian, O., Dorafshan, S., Soofiani, N.M. Optimization of malachite green biosorption by green microalgae from aqueous solutions (2014) Journal of Environmental Studies, 40 (1), pp. 163-176.

Cassotti, M., Consonni, V., Mauri, A., Ballabio, D. (2014) Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia Magna. SAR and QSAR in Environmental Research, 25, 1013-1036

Ballabio, D., Consonni, V., Mauri, A., Claeys-Bruno, M., Sergent, M., Todeschini, R. (2014) A novel variable reduction method adapted from space-filling designs. Chemometrics and Intelligent Laboratory Systems, 136, 147-154

Sahigara, F., Ballabio, D., Todeschini, R., Consonni, V. (2014). Assessing the validity of QSARs for ready biodegradability of chemicals: An Applicability Domain perspective. Current Computer-Aided Drug Design, 10, 137-147

Todeschini, R., Consonni, V., Ballabio, D., Mauri, A., Cassotti, M. Lee, S., West, A., Cartlidge, D. (2014). QSPR study of rheological and mechanical properties of Chloroprene rubber accelerators. Rubber Chemistry and Technology, 87, 219-238

Cassotti, M., Ballabio, D., Consonni, V., Mauri, A., Tetko, I., Todeschini, R. (2014). Prediction of acute aquatic toxicity toward daphnia magna by using the GA-kNN method. ATLA: Alternatives to Laboratory Animals, 42, 31-41

Adhikari N., Halder A.K., Mondal C., Jha T., Ligand based validated comparative chemometric modeling and pharmacophore mapping of aurone derivatives as antimalarial agents, Current Computer-Aided Drug Design, 2013;9:417-432

Aguiar-Pulido V., Gestal M., Cruz-Monteagudo M., Rabunal J.R., Dorado J., Munteanu C.R., Evolutionary computation and QSAR research, Current Computer-Aided Drug Design, 2013;9:206-225

Alonso N., Caamano O., Romero-Duran F.J., Luan F., D. S. Cordeiro M.N., Yanez M., Gonzalez-Diaz H., Garcia-Mera X., Model for high-throughput screening of multitarget drugs in chemical neurosciences: Synthesis, assay, and theoretic study of rasagiline carbamates, ACS Chemical Neuroscience, 2013;4:1393-1403

Andrada M.F., Duchowicz P.R., Castro E.A., QSAR applications on polycyclic aromatic hydrocarbons and some derivatives, Current Organic Chemistry, 2013;17:2872-2879

Barigye S.J., Marrero-Ponce Y., Martinez Santiago O., Martinez Lopez Y., Perez-Gimenez F., Torrens F., Shannon's, mutual, conditional and joint entropy information indices: Generalization of global indices defined from local vertex invariants, Current Computer-Aided Drug Design, 2013;9:164-183

Barigye S.J., Marrero-Ponce Y., Martinez-Lopez Y., Torrens F., Artiles-Martinez L.M., Pino-Urias R.W., Martinez-Santiago O., Relations frequency hypermatrices in mutual, conditional and joint entropy-based information indices, Journal of Computational Chemistry, 2013;34:259-274

Basak S.C., Mathematical descriptors for the prediction of property, bioactivity, and toxicity of chemicals from their structure: A chemical-cum-biochemical approach, Current Computer-Aided Drug Design, 2013;9:449-462

Bazl R., Ganjali M.R., Derakhshankhah H., Saboury A.A., Amanlou M., Norouzi P., Prediction of tyrosinase inhibition for drug design using the genetic algorithm-multiple linear regressions, Medicinal Chemistry Research, 2013;22:5453-5465

Bolboaca S.D., Jantschi L., Diudea M.V., Molecular design and QSARs/QSPRs with molecular descriptors family, Current Computer-Aided Drug Design, 2013;9:195-205

Boronova K., Lehotay J., Hrobonova K., Armstrong D.W., Study of physicochemical interaction of aryloxyaminopropanol derivatives with teicoplanin and vancomycin phases in view of quantitative structure-property relationship studies, Journal of Chromatography A, 2013;1301:47-

Bozek K., Lengauer T., Sierra S., Kaiser R., Domingues F.S., Analysis of Physicochemical and Structural Properties Determining HIV-1 Coreceptor Usage, PLoS Computational Biology, 2013;9:-

Brandmaier S., Novotarskyi S., Sushko I., Tetko I.V., From descriptors to predicted properties: Experimental design by using applicability domain estimation, ATLA Alternatives to Laboratory Animals, 2013;41:33-47

Cao D.-S., Xu Q.-S., Hu Q.-N., Liang Y.-Z., ChemoPy: Freely available python package for computational biology and chemoinformatics, Bioinformatics, 2013;29:1092-1094

Cao D.-S., Zhou G.-H., Liu S., Zhang L.-X., Xu Q.-S., He M., Liang Y.-Z., Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures, Analytica Chimica Acta, 2013;792:18-

Cao H., Wang R., A new method for predicting the net heat of combustion of organic compounds, Advanced Materials Research, 2013;651:215-

Cassani S., Kovarich S., Papa E., Roy P.P., van der Wal L., Gramatica P., Daphnia and fish toxicity of (benzo)triazoles: Validated QSAR models, and interspecies quantitative activity-activity modelling, Journal of Hazardous Materials, 2013;258-259:60-

Castillo-Gonzalez D., Perez-Machado G., Guedin A., Mergny J.-L., Cabrera-Perez M.-A., FDA-approved drugs selected using virtual screening bind specifically to G-quadruplex DNA, Current Pharmaceutical Design, 2013;19:2164-2173

Chen Q., Wu L., Liu W., Xing L., Fan X., Enhanced QSAR model performance by integrating structural and gene expression information, Molecules, 2013;18:10789-10801

Claeys L., Iaccino F., Janssen C.R., Van Sprang P., Verdonck F., Development and validation of a quantitative structure-activity relationship for chronic narcosis to fish, Environmental Toxicology and Chemistry, 2013;32:2217-2225

Correa-Basurto J., Bello M., Rosales-Hernandez M.C., Hernandez-Rodriguez M., Nicolas-Vazquez I., Rojo-Dominguez A., Trujillo-Ferrara J.G., Miranda R., Flores-Sandoval C.A., QSAR, docking, dynamic simulation and quantum mechanics studies to explore the recognition properties of cholinesterase binding sites, Chemico-Biological Interactions, 2014;209:1-13

Cruz-Monteagudo M., Romero Y., Cordeiro M.N.D.S., Borges F., Desirability-based multi-criteria virtual screening of selective antimicrobial cyclic ß-hairpin Cationic Peptidomimetics, Current Pharmaceutical Design, 2013;19:2148-2163

Damale M.G., Harke S.N., Khan F.A.K., Shinde D.B., Sangshetti J.N., Recent advances in multidimensional QSAR (4D-6D): A critical review, Mini-Reviews in Medicinal Chemistry, 2014;14:35-55

Dander A., Mueller L.A.J., Gallasch R., Pabinger S., Emmert-Streib F., Graber A., Dehmer M., [COMMODE] a large-scale database of molecular descriptors using compounds from PubChem, Source Code for Biology and Medicine, 2013;8:-

D'Archivio A.A., Giannitto A., Maggi M.A., Cross-column prediction of gas-chromatographic retention of polybrominated diphenyl ethers, Journal of Chromatography A, 2013;1298:131-

Das R.N., Sanderson H., Mwambo A.E., Roy K., Preliminary studies on model development for rodent toxicity and its interspecies correlation with aquatic toxicities of pharmaceuticals, Bulletin of Environmental Contamination and Toxicology, 2013;90:375-381

Dashtbozorgi Z., Golmohammadi H., Konoz E., Support vector regression based QSPR for the prediction of retention time of pesticide residues in gas chromatography-mass spectroscopy, Microchemical Journal, 2013;106:60-

Debnath U., Katti S.B., Prabhakar Y.S., Graph theory concepts in the rationales of anti HIV-1 compounds, Current Computer-Aided Drug Design, 2013;9:472-481

Duardo-Sanchez A., Gonzalez-Diaz H., Legal issues for chem-bioinformatics models, Frontiers in Bioscience - Elite, 2013;5 E:361-374

Durjava M.K., Kolar B., Arnus L., Papa E., Kovarich S., Sahlin U., Peijnenburg W., Experimental assessment of the environmental fate and effects of triazoles and benzotriazole, ATLA Alternatives to Laboratory Animals, 2013;41:65-75

Dutt R., Madan A.K., Models for the prediction of PPARs agonistic activity of indanylacetic acids, Medicinal Chemistry Research, 2013;22:3213-3228

Galvez-Llompart M., Zanni R., Romualdi P., Garcia-Domenech R., Selection of nutraceutical compounds as COX inhibitors by molecular topology, Medicinal Chemistry Research, 2013;22:3466-3477

Garcia I., Update of QSAR & docking & alignment studies of the DNA polymerase inhibitors, Current Bioinformatics, 2013;8:472-482

Garcia-Domenech R., Zanni R., Galvez-Llompart M., De Julian-Ortiz J.V., Modeling anti-allergic natural compounds by molecular topology, Combinatorial Chemistry and High Throughput Screening, 2013;16:628-635

Garkani-Nejad Z., Ahmadi-Roudi B., Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives, Canadian Journal of Chemistry, 2013;91:255-262

Gharagheizi F., Mirkhani S.A., Keshavarz M.H., Farahani N., Tumba K., A molecular-based model for prediction of liquid viscosity of pure organic compounds: A quantitative structure property relationship (QSPR) approach, Journal of the Taiwan Institute of Chemical Engineers, 2013;44:359-364

Ghavami R., Rasouli Z., Investigation of retention behavior of anthraquinoids in RP-HPLC on 17 different C18 stationary phases by means of quantitative structure retention relationships, Medicinal Chemistry Research, 2013;22:2677-2691

Giaginis C., Tsantili-Kakoulidou A., Quantitative structure-retention relationships as useful tool to characterize chromatographic systems and their potential to simulate biological processes, Chromatographia, 2013;76:211-226

Golmohammadi H., Dashtbozorgi Z., Acree Jr. W.E., QSPR models for prediction of gas-to-heptane and gas-to-hexadecane solvation enthalpies of organic compounds from theoretical molecular descriptors, Structural Chemistry, 2013;24:1799-1810

Gonzalez-Diaz H., Arrasate S., Gomez-San A.J., Sotomayor N., Lete E., Besada-Porto L., Ruso J.M., General theory for multiple input-output perturbations in complex molecular systems. 1. linear QSPR electronegativity models in physical, organic, and medicinal chemistry, Current Topics in Medicinal Chemistry, 2013;13:1713-1741

Gonzalez-Diaz H., Arrasate S., Sotomayor N., Lete E., Munteanu C.R., Pazos A., Besada-Porto L., Ruso J.M., MIANN models in medicinal, Physical and Organic Chemistry, Current Topics in Medicinal Chemistry, 2013;13:619-641

Goodarzi M., Saeys W., De Araujo M.C.U., Galvao R.K.H., Vander Heyden Y., Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique, European Journal of Pharmaceutical Sciences, 2014;51:189-195

Gorynski K., Bojko B., Nowaczyk A., Bucinski A., Pawliszyn J., Kaliszan R., Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds, Analytica Chimica Acta, 2013;797:19-

Gramatica P., On the development and validation of QSAR models, Methods in Molecular Biology, 2013;930:526-

Gramatica P., Chirico N., Papa E., Cassani S., Kovarich S., QSARINS: A new software for the development, analysis, and validation of QSAR MLR models, Journal of Computational Chemistry, 2013;34:2121-2132

Gu T., Yang X., Li M., Wu M., Su Q., Lu W., Zhang Y., Predicting the DPP-IV inhibitory activity pIC50 based on their physicochemical properties, BioMed Research International, 2013;2013:-

Gupta A.K., Singh P., Sabarwal N., Rationalization of molecular descriptors of aurone analogs toward anti-malarial activity, Asian Journal of Pharmaceutical and Clinical Research, 2014;7:186-192

Gupta M.K., CP-MLR/PLS-directed QSAR studies on the antimalarial activity and cytotoxicity of substituted 4-aminoquinolines, Medicinal Chemistry Research, 2013;22:3497-3509

Gupta R.A., Kaskhedikar S.G., Synthesis, antitubercular activity, and QSAR analysis of substituted nitroaryl analogs: Chalcone, pyrazole, isoxazole, and pyrimidines, Medicinal Chemistry Research, 2013;22:3863-3880

Hartmman A.P., Jornada D.H., De Melo E.B., A new, fully validated and interpreted quantitative structure-activity relationship model of p-aminosalicylic acid derivatives as neuraminidase inhibitors, Chemical Papers, 2013;67:556-567

Helguera A.M., Perez-Garrido A., Gaspar A., Reis J., Cagide F., Vina D., Cordeiro M.N.D.S., Borges F., Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors, European Journal of Medicinal Chemistry, 2013;59:90-

Huang J., Fan X., Reliably assessing prediction reliability for high dimensional QSAR data, Molecular Diversity, 2013;17:63-73

Ivan D., Crisan L., Funar-Timofei S., Mracec M., A quantitative structure-activity relationships study for the anti-HIV-1 activities of 1-[(2-hydroxyethoxy)methyl]-6- -(phenylthio)thymine derivatives using the multiple linear regression and partial least squares methodologies, Journal of the Serbian Chemical Society, 2013;78:495-506

Joo R., Bertrand S., Tam J., Fablet R., Hidden Markov Models: The Best Models for Forager Movements?, PLoS ONE, 2013;8:-

Juretic D., Kusic H., Papic A., Smidt M., Jezovita O., Peternel I., Bozic A.L., Modeling of photodegradation kinetics of aromatic pollutants in water matrix, Journal of Photochemistry and Photobiology A: Chemistry, 2013;271:76-

Kabankin A.S., Radkevich L.A., Collective recognition strategy for estimating hepatoprotector activity of various chemical compounds in increasing liver repair potential, Pharmaceutical Chemistry Journal, 2013;47:181-186

Kar S., Roy K., Predictive chemometric modeling and three-dimensional toxicophore mapping of diverse organic chemicals causing bioluminescent repression of the bacterium genus pseudomonas, Industrial and Engineering Chemistry Research, 2013;52:17648-17657

Kar S., Roy K., Prediction of milk/plasma concentration ratios of drugs and environmental pollutants using in silico tools: Classification and regression based QSARs and pharmacophore mapping, Molecular Informatics, 2013;32:693-705

Kar S., Roy K., First report on predictive chemometric modeling, 3D-toxicophore mapping and in silico screening of in vitro basal cytotoxicity of diverse organic chemicals, Toxicology in Vitro, 2013;27:597-608

Karimi H., Farmany A., Noorizadeh H., Chemometrics analysis for investigation of retention behavior of hazardous compounds in effluents, Environmental Monitoring and Assessment, 2013;185:473-483

Keefer C.E., Kauffman G.W., Gupta R.R., Interpretable, probability-based confidence metric for continuous quantitative structure-activity relationship models, Journal of Chemical Information and Modeling, 2013;53:368-383

Kim J., Kim S., Schaumann G.E., Development of QSAR-based two-stage prediction model for estimating mixture toxicity, SAR and QSAR in Environmental Research, 2013;24:841-861

Kleandrova V.V., Speck-Planche A., Regulatory issues in management of chemicals in OECD member countries, Frontiers in Bioscience - Elite, 2013;5 E:375-398

Kumar V., Gupta M.K., Singh G., Prabhakar Y.S., CP-MLR/PLS directed QSAR study on the glutaminyl cyclase inhibitory activity of imidazoles: rationales to advance the understanding of activity profile, Journal of Enzyme Inhibition and Medicinal Chemistry, 2013;28:515-522

Lang K.L., Silva I.T., Machado V.R., Zimmermann L.A., Caro M.S.B., Simoes C.M.O., Schenkel E.P., Duran F.J., Bernardes L.S.C., De Melo E.B., Multivariate SAR and QSAR of cucurbitacin derivatives as cytotoxic compounds in a human lung adenocarcinoma cell line, Journal of Molecular Graphics and Modelling, 2014;48:79-

Lei B., Li J., Yao X., A novel strategy of structural similarity based consensus modeling, Molecular Informatics, 2013;32:599-608

Li C., Yang X., Li X., Chen J., Qiao X., Development of a model for predicting hydroxyl radical reaction rate constants of organic chemicals at different temperatures, Chemosphere, 2014;95:618-

Li J., Liu H., Huo X., Gramatica P., Structure-activity relationship analysis of the thermal stabilities of nitroaromatic compounds following different decomposition mechanisms, Molecular Informatics, 2013;32:193-202

Luan F., Cordeiro M.N.D.S., Alonso N., Garcia-Mera X., Caamano O., Romero-Duran F.J., Yanez M., Gonzalez-Diaz H., TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases, Bioorganic and Medicinal Chemistry, 2013;21:1870-1879

Madan A.K., Bajaj S., Dureja H., Classification models for safe drug molecules, Methods in Molecular Biology, 2013;930:124-

Mallakpour S., Hatami M., Golmohammadi H., QSPR prediction of thermal decomposition property of non-vinyl polymers having a-amino acids moieties, Polymer Bulletin, 2013;70:715-732

Manikpuri A.D., Joshi S., Evaluation of antimicrobial activity and QSAR study of a molecule library of the Mannich bases of Glutarimides, Oxidation Communications, 2013;36:156-175

Mansouri K., Ringsted T., Ballabio D., Todeschini R., Consonni V., Quantitative structure-activity relationship models for ready biodegradability of chemicals, Journal of Chemical Information and Modeling, 2013;53:867-878

Mansourian M., Saghaie L., Fassihi A., Madadkar-Sobhani A., Mahnam K., Linear and nonlinear QSAR modeling of 1,3,8-substituted-9-deazaxanthines as potential selective A2BAR antagonists, Medicinal Chemistry Research, 2013;22:4549-4567

Martin T.M., Grulke C.M., Young D.M., Russom C.L., Wang N.Y., Jackson C.R., Barron M.G., Prediction of aquatic toxicity mode of action using linear discriminant and random forest models, Journal of Chemical Information and Modeling, 2013;53:2229-2239

Mehdikhani A., Lotfizadeh H.R., Arman K., Noorizadeh H., An improved QSPR study of reverse factor of nanoparticles in roadside atmosphere on kernel partial least squares and genetic algorithm, Journal of Theoretical and Computational Chemistry, 2013;12:-

Merida S., Fustero S., Villar V.M., Galvez M., Roman R., Amigo J.M., Efficacy and activity prediction by molecular topology of new drugs against the tetranychus urticae plague, Combinatorial Chemistry and High Throughput Screening, 2013;16:473-483

Mitra I., Saha A., Roy K., Quantification of contributions of different molecular fragments for antioxidant activity of coumarin derivatives based on QSAR analyses, Canadian Journal of Chemistry, 2013;91:428-441

Mitra I., Saha A., Roy K., Predictive modeling of antioxidant coumarin derivatives using multiple approaches: Descriptor-based QSAR, 3D-pharmacophore mapping, and HQSAR, Scientia Pharmaceutica, 2013;81:57-80

Mitra I., Saha A., Roy K., Predictive chemometric modeling of DPPH free radical-scavenging activity of azole derivatives using 2D- and 3D-quantitative structure-activity relationship tools, Future Medicinal Chemistry, 2013;5:261-280

Mitra I., Saha A., Roy K., Predictive chemometric modeling of DPPH free radical-scavenging activity of azole derivatives using 2D- and 3D-quantitative structure-activity relationship tools, Future Medicinal Chemistry, 2013;5:261-280

Mohseni Bababdani B., Mousavi M., Gravitational search algorithm: A new feature selection method for QSAR study of anticancer potency of imidazo[4,5-b]pyridine derivatives, Chemometrics and Intelligent Laboratory Systems, 2013;122:11-

Molina E., Uriarte E., Santana L., Matos M.J., Borges F., QSAR and complex network recognition of miRNAs in stem cells, Current Bioinformatics, 2013;8:438-451

Najafi A., Sobhanardakani S., Multivariate modeling of cytochrome P450 enzymes for 4-aminoquinoline antimalarial analogues using genetic-algorithms multiple linear regression, Tropical Journal of Pharmaceutical Research, 2013;12:905-912

Nandy A., Kar S., Roy K., Development and validation of regression-based QSAR models for quantification of contributions of molecular fragments to skin sensitization potency of diverse organic chemicals, SAR and QSAR in Environmental Research, 2013;24:1009-1023

Nandy A., Kar S., Roy K., Linear discriminant analysis for skin sensitisation potential of diverse organic chemicals, Molecular Simulation, 2013;39:432-441

Nandy A., Kar S., Roy K., Development of classification-and regression-based QSAR models and in silico screening of skin sensitisation potential of diverse organic chemicals, Molecular Simulation, 2014;40:261-274

Nantasenamat C., Worachartcheewan A., Prachayasittikul S., Isarankura-Na-Ayudhya C., Prachayasittikul V., QSAR modeling of aromatase inhibitory activity of 1-substituted 1,2,3-triazole analogs of letrozole, European Journal of Medicinal Chemistry, 2013;69:114-

Nikolic K., Filipic S., Smolinski A., Kaliszan R., Agbaba D., Partial least square and hierarchical clustering in ADMET modeling: Prediction of blood - Brain barrier permeation of a-adrenergic and imidazoline receptor ligands, Journal of Pharmacy and Pharmaceutical Sciences, 2013;16:622-647

Noorizadeh H., Sajjadifar S., Farmany A., A quantitative structure-activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models, Medicinal Chemistry Research, 2013;22:5442-5452

Ohno H., Araho D., Uesawa Y., Kagaya H., Ishihara M., Sakagami H., Yamamoto M., Evaluation of cytotoxiciy and tumor-specificity of licorice flavonoids based on chemical structure, Anticancer Research, 2013;33:3061-3068

Ojha P.K., Roy K., First report on exploring structural requirements of 1,2,3,4- tetrahydroacridin-9(10h)-one analogs as antimalarials using multiple QSAR approaches: Descriptor-based QSAR, CoMFA-CoMSIA 3DQSAR, HQSAR and G-QSAR approaches, Combinatorial Chemistry and High Throughput Screening, 2013;16:7-21

Ojha P.K., Roy K., Exploring structural requirements for a class of nucleoside inhibitors (PfdUTPase) as antimalarials: First report on QSAR, pharmacophore mapping and multiple docking studies, Combinatorial Chemistry and High Throughput Screening, 2013;16:739-757

Ojha P.K., Roy K., First report on exploring structural requirements of alpha and beta thymidine analogs for PfTMPK inhibitory activity using in silico studies, BioSystems, 2013;113:177-195

Ojha P.K., Roy K., First report on exploring structural requirements of 1,2,3,4- tetrahydroacridin-9(10h)-one analogs as antimalarials using multiple QSAR approaches: Descriptor-based QSAR, CoMFA-CoMSIA 3DQSAR, HQSAR and G-QSAR approaches, Combinatorial Chemistry and High Throughput Screening, 2013;16:7-21

Oros G., Cserhati T., Support related differential impact of substituents on performance of (alkoxy-phenyl)benzamides in normal phase TLC, Journal of Liquid Chromatography and Related Technologies, 2013;36:2363-2377

Pan Y., Jiang J., Wang R., Zhu X., Zhang Y., A novel method for predicting the flash points of organosilicon compounds from molecular structures, Fire and Materials, 2013;37:130-139

Patel J.R., Prajapati L.M., Predictive QSAR modeling on tetrahydropyrimidine-2-one derivatives as HIV-1 protease enzyme inhibitors, Medicinal Chemistry Research, 2013;22:2795-2801

Petric M., Crisan L., Crisan M., Micle A., Maranescu B., Ilia G., Synthesis and QSRR study for a series of phosphoramidic acid derivatives, Heteroatom Chemistry, 2013;24:138-145

Pham-The H., Garrigues T., Bermejo M., Gonzalez-Alvarez I., Monteagudo M.C., Cabrera-Perez M.A., Provisional classification and in silico study of biopharmaceutical system based on Caco-2 cell permeability and dose number, Molecular Pharmaceutics, 2013;10:2445-2461

Pingaew R., Worachartcheewan A., Nantasenamat C., Prachayasittikul S., Ruchirawat S., Prachayasittikul V., Synthesis, cytotoxicity and QSAR study of N-tosyl-1,2,3,4- tetrahydroisoquinoline derivatives, Archives of Pharmacal Research, 2013;36:1066-1077

Podunavac-Kuzmanovic S.O., Cvetkovic D.D., Jevric L.R., Uzelac N.J., Quantitative structure-activity relationship (QSAR) study of a series of benzimidazole derivatives as inhibitors of Saccharomyces Cerevisiae, Acta Chimica Slovenica, 2013;60:26-33

Poole C.F., Ariyasena T.C., Lenca N., Estimation of the environmental properties of compounds from chromatographic measurements and the solvation parameter model, Journal of Chromatography A, 2013;1317:104-

Popa L., Draganescu D., Albu M.G., Ortan A., Ghica M.V., BIOINFOQSAR - A specialized software developed for QSAR models. Predictable capacity testing for conventional antitumor drugs, Farmacia, 2013;61:427-438

Prado-Prado F., Garcia-Mera X., Rodriguez-Borges J.E., Concu R., Perez-Montoto L.G., Gonzalez-Diaz H., Duardo-Sanchez A., Patents of bio-active compounds based on computer-aided drug discovery techniques, Frontiers in Bioscience - Elite, 2013;5 E:399-407

Prado-Prado F.J., Escobar M., Garcia-Mera X., Review of bioinformatics and theoretical studies of acetylcholinesterase inhibitors, Current Bioinformatics, 2013;8:496-510

Ray S., Roy K., Modeling adsorption of organic compounds on activated carbon using ETA indices, Chemical Engineering Science, 2013;104:438-

Riera-Fernandez P., Munteanu C.R., Martin-Romalde R., Duardo-Sanchez A., Gonzalez-Diaz H., Markov-Randic indices for QSPR re-evaluation of metabolic, parasite-host, fasciolosis spreading, brain cortex and legal-social complex networks, Current Bioinformatics, 2013;8:401-415

Ring J.R., Zheng F., Haubner A.J., Littleton J.M., Crooks P.A., Improving the inhibitory activity of arylidenaminoguanidine compounds at the N-methyl-d-aspartate receptor complex from a recursive computational- experimental structure-activity relationship study, Bioorganic and Medicinal Chemistry, 2013;21:1764-1774

Roy K., Kabir H., QSPR with extended topochemical atom (ETA) indices: Exploring effects of hydrophobicity, branching and electronic parameters on logCMC values of anionic surfactants, Chemical Engineering Science, 2013;87:151-

Ruan Z.-X., Huangfu D.-S., Xu X.-J., Sun P.-H., Chen W.-M., 3D-QSAR and molecular docking for the discovery of ketolide derivatives, Expert Opinion on Drug Discovery, 2013;8:427-444

Saghaie L., Sakhi H., Sabzyan H., Shahlaei M., Shamshirian D., Stepwise MLR and PCR QSAR study of the pharmaceutical activities of antimalarial 3-hydroxypyridinone agents using B3LYP/6-311++G* *descriptors, Medicinal Chemistry Research, 2013;22:1679-1688

Sahigara F., Ballabio D., Todeschini R., Consonni V., Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions, Journal of Cheminformatics, 2013;5:-

Sahigara F., Ballabio D., Todeschini R., Consonni V., Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions, Journal of Cheminformatics, 2013;5:-

Schafer K.N., Cisek K., Huseby C.J., Chang E., Kuret J., Structural determinants of tau aggregation inhibitor potency, Journal of Biological Chemistry, 2013;288:32599-32611

Shahlaei M., Fassihi A., QSAR analysis of some 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 inhibitors using genetic algorithm-least square support vector machine, Medicinal Chemistry Research, 2013;22:4384-4400

Shahlaei M., Pourhossein A., Modeling of CCR5 antagonists as anti HIV agents using combined genetic algorithm and adaptive neuro-fuzzy inference system (GA-ANFIS), Medicinal Chemistry Research, 2013;22:4423-4436

Shahlaie M., Fassihi A., Pourhossein A., Arkan E., Statistically validated QSAR study of some antagonists of the human CCR5 receptor using least square support vector machine based on the genetic algorithm and factor analysis, Medicinal Chemistry Research, 2013;22:1399-1414

Sharma B.K., Verma S., Prabhakar Y.S., Topological and physicochemical characteristics of 1,2,3,4-tetrahydroacridin-9(10H)-ones and their antimalarial profiles: A composite insight to the structure-activity relation, Current Computer-Aided Drug Design, 2013;9:317-335

Singh G., Gupta M.K., Kumar V., Prabhakar Y.S., Modeling of LIM-kinase 2 inhibitory activity of pyrrolopyrimidine analogues: Useful in treatment of ocular hypertension and glaucoma, Medicinal Chemistry, 2013;9:402-409

Singh P., Molecular descriptors in modeling of TNF-a converting enzyme (TACE) inhibition activity of 2-(2-aminothiazol-4-yl)pyrrolidine-based tartrate diamides, Indian Journal of Chemistry - Section B Organic and Medicinal Chemistry, 2013;52:1325-1341

Singh P., Molecular descriptors in modelling the tumour necrosis factor-a converting enzyme inhibition activity of novel tartrate-based analogues, Indian Journal of Pharmaceutical Sciences, 2013;75:36-44

Singh P., Molecular descriptors in modeling of TNF-a converting enzyme (TACE) inhibition activity of 2-(2-aminothiazol-4-yl)pyrrolidine-based tartrate diamides, Indian Journal of Chemistry - Section B Organic and Medicinal Chemistry, 2013;52:1325-1341

Singh S., Supuran C.T., Chemometric QSAR modeling and in silico design of carbonic anhydrase inhibition of a coral secretory isoform by sulfonamide, Bioorganic and Medicinal Chemistry, 2013;21:1495-1502

Speck-Planche A., Cordeiro M.N.D.S., Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents, Current Topics in Medicinal Chemistry, 2013;13:3101-3117

Speck-Planche A., Kleandrova V.V., Cordeiro M.N.D.S., New insights toward the discovery of antibacterial agents: Multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs, European Journal of Pharmaceutical Sciences, 2013;48:812-818

Speck-Planche A., Kleandrova V.V., Luan F., Cordeiro M.N.D.S., Unified multi-target approach for the rational in silico design of anti-bladder cancer agents, Anti-Cancer Agents in Medicinal Chemistry, 2013;13:791-800

Speck-Planche A., Kleandrova V.V., Luan F., Cordeiro M.N.D.S., Multi-target inhibitors for proteins associated with Alzheimer: In silico discovery using fragment-based descriptors, Current Alzheimer Research, 2013;10:117-124

Speck-Planche A., Kleandrova V.V., Scotti M.T., Cordeiro M.N.D.S., 3D-QSAR methodologies and molecular modeling in bioinformatics for the search of novel anti-HIV therapies: Rational design of entry inhibitors, Current Bioinformatics, 2013;8:452-464

Srivastava A.K., Srivastava A., Shukla N., QSAR study on tie-2 inhibitors: Dominating role of topological parameters, Oxidation Communications, 2013;36:143-155

Tanabe K., Kurita T., Nishida K., Lucic B., Amic D., Suzuki T., Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models, SAR and QSAR in Environmental Research, 2013;24:565-580

Tenorio-Borroto E., Penuelas-Rivas C.G., Vasquez-Chagoyan J.C., Castanedo N., Prado-Prado F.J., Garcia-Mera X., Gonzalez-Diaz H., Model for high-throughput screening of drug immunotoxicity - Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry, European Journal of Medicinal Chemistry, 2014;72:220-

Thompson D., Batterham A.M., Towards Integrated Physical Activity Profiling, PLoS ONE, 2013;8:-

Toropov A.A., Toropova A.P., Benfenati E., Gini G., OCWLGI descriptors: Theory and praxis, Current Computer-Aided Drug Design, 2013;9:226-232

Toropov A.A., Toropova A.P., Raska I., Leszczynska D., Leszczynski J., Comprehension of drug toxicity: Software and databases, Computers in Biology and Medicine, 2014;45:20-25

Vallianatou T., Lambrinidis G., Tsantili-Kakoulidou A., In silico prediction of human serum albumin binding for drug leads, Expert Opinion on Drug Discovery, 2013;8:583-595

Whitebay E.A., Gasem K.A.M., Neely B.J., Ramsey J.D., In silico prediction of mechanism of action for cancer therapeutics, Molecular Informatics, 2013;32:735-741

Worachartcheewan A., Nantasenamat C., Isarankura-Na-Ayudhya C., Prachayasittikul V., QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum, Chemical Papers, 2013;67:1462-1473

Worachartcheewan A., Nantasenamat C., Owasirikul W., Monnor T., Naruepantawart O., Janyapaisarn S., Prachayasittikul S., Prachayasittikul V., Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression, European Journal of Medicinal Chemistry, 2014;73:264-

Xu J., Zhu L., Fang D., Liu L., Bai Z., Wang L., Xu W., A simple QSPR model for the prediction of the adsorbability of organic compounds onto activated carbon cloth, SAR and QSAR in Environmental Research, 2013;24:47-59

Yang X., Li M., Su Q., Wu M., Gu T., Lu W., QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes, Medicinal Chemistry Research, 2013;22:5274-5283

Yang X., Xie H., Chen J., Li X., Anionic phenolic compounds bind stronger with transthyretin than their neutral forms: Nonnegligible mechanisms in virtual screening of endocrine disrupting chemicals, Chemical Research in Toxicology, 2013;26:1340-1347

Yang Y., Wang J., Li Y., Xiao W., Wang Z., Zhang J., Gao W., Zhang S., Yang L., Structure determinants of indolin-2-on-3-spirothiazolidinones as MptpB inhibitors: An in silico study, Soft Matter, 2013;9:11054-11077

Yu X., Yu R., Setschenow constant prediction based on the IEF-PCM calculations, Industrial and Engineering Chemistry Research, 2013;52:11182-11188

Zare-Shahabadi V., Lotfizadeh M., Gandomani A.R.A., Papari M.M., Determination of boiling points of azeotropic mixtures using quantitative structure-property relationship (QSPR) strategy, Journal of Molecular Liquids, 2013;188:27-

Zhang L., Fourches D., Sedykh A., Zhu H., Golbraikh A., Ekins S., Clark J., Connelly M.C., Sigal M., Hodges D., Guiguemde A., Guy R.K., Tropsha A., Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening, Journal of Chemical Information and Modeling, 2013;53:475-492

Zheng F., Zhan M., Huang X., Abdul Hameed M.D.M., Zhan C.-G., Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches, Bioorganic and Medicinal Chemistry, 2014;22:538-549

Kar S, Deeb O, Roy K. Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor. Ecotoxicol Environ Saf. 2012;82:85-95.

Dashtbozorgi Z, Golmohammadi H, Acree Jr. WE. Prediction of gas to water solvation enthalpy of organic compounds using support vector machine. Thermochimica Acta. 2012;539:7-15.

Goyal RK, Dureja H, Singh G, Madan AK. Models for anti-inflammatory activity of 8-substituted-4-anilino-6- aminoquinoline-3-carbonitriles. Medicinal Chemistry Research. 2012;21(7):1044-55.

Gharagheizi F, Gohar MRS, Vayeghan MG. A quantitative structure-property relationship for determination of enthalpy of fusion of pure compounds. Journal of Thermal Analysis and Calorimetry. 2012;109(1):501-6.

Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. Chemoinformatics in multi-target drug discovery for anti-cancer therapy: In silico design of potent and versatile anti-brain tumor agents. Anti-Cancer Agents in Medicinal Chemistry. 2012;12(6):678-85.

Jeliazkova N. Web tools for predictive toxicology model building. Expert Opinion on Drug Metabolism and Toxicology. 2012;8(7):791-801.

Ahmadi S. A QSPR study of association constants of macrocycles toward sodium cation. Macroheterocycles. 2012;5(1):23-31.

Roy K, Mitra I. Electrotopological state atom (E-state) index in drug design, QSAR, property prediction and toxicity assessment. Current Computer-Aided Drug Design. 2012;8(2):135-58.

Xu J, Zhu L, Fang D, Wang L, Xiao S, Liu L, et al. QSPR studies of impact sensitivity of nitro energetic compounds using three-dimensional descriptors. J Mol Graph Model. 2012;36:10-9.

Stanton DT. QSAR and QSPR model interpretation using partial least squares (PLS) analysis. Current Computer-Aided Drug Design. 2012;8(2):107-27.

Roy K, Mitra I. On the use of the metric r m 2 as an effective tool for validation of QSAR models in computational drug design and predictive toxicology. Mini-Reviews in Medicinal Chemistry. 2012;12(6):491-504.

Prado-Prado F, García I. Review of theoretical studies for prediction of neurodegenerative inhibitors. Mini-Reviews in Medicinal Chemistry. 2012;12(6):452-66.

Bagheri M, Borhani TNG, Zahedi G. Estimation of flash point and autoignition temperature of organic sulfur chemicals. Energy Conversion and Management. 2012;58:185-96.

Hao M, Zhang S, Qiu J. Toward the prediction of FBPase inhibitory activity using chemoinformatic methods. International Journal of Molecular Sciences. 2012;13(6):7015-37.

Le T, Epa VC, Burden FR, Winkler DA. Quantitative structure-property relationship modeling of diverse materials properties. Chem Rev. 2012;112(5):2889-919.

Roy K, Kabir H. QSPR with extended topochemical atom (ETA) indices: Modeling of critical micelle concentration of non-ionic surfactants. Chemical Engineering Science. 2012;73:86-98.

Fatemi MH, Moghaddam MR. Quantitative structure-property prediction of ion-molecule rate constants for proton transfer reaction between H 3O + and volatile organic compound. Journal of Mass Spectrometry. 2012;47(5):574-80.

Shahlaei M, Madadkar-Sobhani A, Saghaie L, Fassihi A. Application of an expert system based on genetic algorithm-adaptive neuro-fuzzy inference system (GA-ANFIS) in QSAR of cathepsin K inhibitors. Expert Syst Appl. 2012;39(6):6182-91.

Crisan L, Alina Bora LP, Avram S, Kurunczi L. PLS (partial least square) study for GSK-3 (glycogen synthase kinase-3) inhibition by indirubin derivatives. Rev Chim. 2012;63(5):481-8.

Mitra I, Saha A, Roy K. Development of multiple QSAR models for consensus predictions and unified mechanistic interpretations of the free-radical scavenging activities of chromone derivatives. Journal of Molecular Modeling. 2012;18(5):1819-40.

Azimi G, Afiuni-Zadeh S, Karami A. A QSAR study for modeling of thyroid receptors ß1 selective ligands by application of adaptive neuro-fuzzy inference system and radial basis function. J Chemometrics. 2012;26(5):135-42.

Combes RD. In silico methods for toxicity prediction [Internet]; 2012

Gharagheizi F, Ilani-Kashkouli P, Mirkhani SA, Mohammadi AH. Computation of upper flash point of chemical compounds using a chemical structure-based model. Industrial and Engineering Chemistry Research. 2012;51(13):5103-7.

Riera-Fernández P, Martín-Romalde R, Prado-Prado FJ, Escobar M, Munteanu CR, Concu R, et al. From QSAR models of drugs to complex networks: State-of-art review and introduction of new markov-spectral moments indices. Current Topics in Medicinal Chemistry. 2012;12(8):927-60.

Goudarzi N, Goodarzi M, Chen T. QSAR prediction of HIV inhibition activity of styrylquinoline derivatives by genetic algorithm coupled with multiple linear regressions. Medicinal Chemistry Research. 2012;21(4):437-43.

García I, Fall Y, Gómez G. Review of synthesis, biological assay, and QSAR studies of HMGR inhibitors. Current Topics in Medicinal Chemistry. 2012;12(8):895-919.

Tugcu G, Saçan MT, Vracko M, Novic M, Minovski N. QSTR modelling of the acute toxicity of pharmaceuticals to fish. SAR QSAR Environ Res. 2012;23(3-4):297-310.

Luan F, Borges F, Cordeiro MNDS. Recent advances on A 3 adenosine receptor antagonists by QSAR tools. Current Topics in Medicinal Chemistry. 2012;12(8):878-94.

González-Díaz H. QSAR/QSPR models as enabling technologies for drug & targets discovery in: Medicinal chemistry, microbiology-parasitology, neurosciences, bioinformatics, proteomics and other biomedical sciences. Current Topics in Medicinal Chemistry. 2012;12(8):799-801.

Ramrez-Galicia G, Garduo-Juárez R, Correa-Basurto J, Deeb O. Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations. Journal of Enzyme Inhibition and Medicinal Chemistry. 2012;27(2):174-86.

Kar S, Roy K. First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines. Chemosphere. 2012;87(4):339-55.

Dwivedi SD, Bharadwaj A, Ghuraiya A, Shrivastava A. Topological investingation and modeling of antimalarial activity of chalcone. International Journal of ChemTech Research. 2012;4(2):662-8.

Speck-Planche A, Cordeiro MNDS. Computer-aided drug design methodologies toward the design of anti-hepatitis C agents. Current Topics in Medicinal Chemistry. 2012;12(8):802-13.

Li Y, Hao M, Ren H, Zhang S, Wang X, Ma M, et al. Exploring the structure requirement for PKC? inhibitory activity of pyridinecarbonitrile derivatives: An in silico analysis. J Mol Graph Model. 2012;34:76-88.

Niño H, Rodríguez-Borges JE, García-Mera X, Prado-Prado F. Review of synthesis, assay, and prediction of ß and ?-secretase inhibitors. Current Topics in Medicinal Chemistry. 2012;12(8):828-44.

Speck-Planche A, Luan F, Cordeiro MNDS. Role of ligand-based drug design methodologies toward the discovery of new anti-alzheimer agents: Futures perspectives in fragment-based ligand design. Curr Med Chem. 2012;19(11):1635-45.

Dave K, Lahiry A. Conotoxins: Review and docking studies to determine potentials of conotoxin as an anticancer drug molecule. Current Topics in Medicinal Chemistry. 2012;12(8):845-51.

Tarko L, Putz MV. On quantitative structuretoxicity relationships (QSTR) using high chemical diversity molecules group. Journal of Theoretical and Computational Chemistry. 2012;11(2):265-72.

Yu X, Wang X. Prediction of glass transition temperatures of aromatic heterocyclic polymers. International Journal of Materials Research. 2012;103(3):329-35.

Bagheri M, Rajabi M, Mirbagheri M, Amin M. BPSO-MLR and ANFIS based modeling of lower flammability limit. J Loss Prev Process Ind. 2012;25(2):373-82.

Nowaczyk A, Kulig K. QSAR studies on a number of pyrrolidin-2-one antiarrhythmic arylpiperazinyls. Medicinal Chemistry Research. 2012;21(3):373-81.

González-Díaz H, Munteanu CR, Postelnicu L, Prado-Prado F, Gestal M, Pazos A. LIBP-pred: Web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria. Molecular BioSystems. 2012;8(3):851-62.

González-Díaz H, Concu R, Pérez-Montoto LG, Ubeira FM, Romaris F, Paniagua E, et al. Generalized string pseudo-folding lattices in bioinformatics: State-of-art review, new model for enzyme sub-classes, and study of ESTs on trichinella spiralis. Current Bioinformatics. 2012;7(1):7-34.

Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H. Comparative studies on some metrics for external validation of QSPR models. Journal of Chemical Information and Modeling. 2012;52(2):396-408.

Speck-Planche A, Kleandrova VV, Scotti MT. Fragment-based approach for the in silico discovery of multi-target insecticides. Chemometrics Intellig Lab Syst. 2012;111(1):39-45.

Gharagheizi F. Determination of diffusion coefficient of organic compounds in water using a simple molecular-based method. Industrial and Engineering Chemistry Research. 2012;51(6):2797-803.

Fassihi A, Shahlaei M, Moeinifard B, Sabet R. QSAR study of anthranilic acid sulfonamides as methionine aminopeptidase-2 inhibitors. Monatshefte fur Chemie. 2012;143(2):189-98.

Shariati-Rad M, Hasani M. QSPR study of charge-transfer complexes of some organic donors with p-chloranil using PLSR and MLR. Journal of the Iranian Chemical Society. 2012;9(1):19-25.

Glávez J, Glávez-Llompart M, García-Domenech R. Molecular topology as a novel approach for drug discovery. Expert Opinion on Drug Discovery. 2012;7(2):133-53.

Noorizadeh H, Farmany A. Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods. Drug Testing and Analysis. 2012;4(2):151-7.

Pasquale G, Romanelli GP, Autino JC, García J, Ortiz EV, Duchowicz PR. Quantitative structure-activity relationships of mosquito larvicidal chalcone derivatives. J Agric Food Chem. 2012;60(2):692-7.

Ibezim E, Duchowicz PR, Ortiz EV, Castro EA. QSAR on aryl-piperazine derivatives with activity on malaria. Chemometrics Intellig Lab Syst. 2012;110(1):81-8.

Xu J, Wang L, Zhang H, Shen X, Liang G. Quantitative structure-property relationships studies on free-radical polymerization chain-transfer constants for styrene. J Appl Polym Sci. 2012;123(1):356-64.

Tseng YJ, Hopfinger AJ, Esposito EX. The great descriptor melting pot: Mixing descriptors for the common good of QSAR models. J Comput Aided Mol Des. 2012;26(1):39-43.

Fatemi MH, Chahi ZG. QSPR-based estimation of the half-lives for polychlorinated biphenyl congeners. SAR QSAR Environ Res. 2012;23(1-2):155-68.

Paukku Y, Hill G. Quantum topological molecular descriptors in QSAR analysis of organophosphorus compounds. International Journal of Quantum Chemistry. 2012;112(5):1343-52.

Barbosa EG, Ferreira MMC. Digital filters for molecular interaction field descriptors. Molecular Informatics. 2012;31(1):75-84.

Shahlaei M, Madadkar-Sobhani A, Fassihi A, Saghaie L, Shamshirian D, Sakhi H. Comparative quantitative structure-activity relationship study of some 1-aminocyclopentyl-3-carboxyamides as CCR2 inhibitors using stepwise MLR, FA-MLR, and GA-PLS. Medicinal Chemistry Research. 2012;21(1):100-15.

Rasulev B, Turabekova M, Gorska M, Kulig K, Bielejewska A, Lipkowski J, et al. Use of quantitative structure-enantioselective retention relationship for the liquid chromatography chiral separation prediction of the series of pyrrolidin-2-one compounds. Chirality. 2012;24(1):72-7.

Liu Y, Tan Z, Zhang S. Prediction of glass transition temperatures of polyquinolines and polyquinoxalines. Polymer Science - Series A. 2012;54(1):48-60.

Jalali-Heravi M, Mani-Varnosfaderani A. Navigating drug-like chemical space of anticancer molecules using genetic algorithms and counterpropagation artificial neural networks. Molecular Informatics. 2012;31(1):63-74.

Roy K, Das RN. QSTR with extended topochemical atom (ETA) indices. 15. development of predictive models for toxicity of organic chemicals against fathead minnow using second-generation ETA indices. SAR QSAR Environ Res. 2012;23(1-2):125-40.

Lapid H, Shushan S, Plotkin A, Voet H, Roth Y, Hummel T, Schneidman E, Sobel N. Neural activity at the human olfactory epithelium reflects olfactory perception. Nature neuroscience. 2011;14(11):1455-6

Yabuuchi H, Niijima S, Takematsu H, Ida T, Hirokawa T, Hara T, Ogawa T, Minowa Y, Tsujimoto G, Okunoa Y. Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Molecular Systems Biology. 2011; 7:47

Cruz-Monteagudo M, Borges F, Cordeiro MNDS. Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics. Journal of Chemical Information and Modeling. 2011;51(12):3060-77

Garro Martinez JC, Duchowicz PR, Estrada MR, Zamarbide GN, Castro EA. QSAR study and molecular design of open-chain enaminones as anticonvulsant agents. International Journal of Molecular Sciences. 2011;12(12):9354-68

García I, Fall Y, Gómez G. Review of QSAR for DNA polymerase inhibitors and new models for heterogeneous series of compounds. Current Computer-Aided Drug Design. 2011;7(4):249-54

Dave K, Gandhi M, Panchal H, Vaidya M. Revision of QSAR, docking, and molecular modeling studies of anti- influenza virus A (H1N1) drugs and targets: Analysis of hemagglutinins 3D structure. Current Computer-Aided Drug Design. 2011;7(4):255-62

Niño H, García-Pintos I, Rodríguez-Borges JE, Escobar-Cubiella M, García-Mera X, Prado-Prado F. Review of synthesis, biological assay and QSAR studies of ß-secretase inhibitors. Current Computer-Aided Drug Design. 2011;7(4):263-75

Riera-Fernández P, Munteanu CR, Dorado J, Martín-Romalde R, Duardo-Sanchez A, González-Díaz H. From chemical graphs in computer-aided drug design to general markov-galvez indices of drug-target, proteome, drug-parasitic disease, technological, and social-legal networks. Current Computer-Aided Drug Design. 2011;7(4):315-37

Speck-Planche A, Cordeiro MNDS. Current drug design of anti-HIV agents through the inhibition of C-C chemokine receptor type 5. Current Computer-Aided Drug Design. 2011;7(4):238-48

Xu J, Wang L, Liang G, Wang L, Shen X. A general quantitative structure-property relationship treatment for dielectric constants of polymers. Polym Eng Sci. 2011;51(12):2408-16

González-Díaz H. QSPR models for computer-aided drug design in microbiology, parasitology, and pharmacology. Current Computer-Aided Drug Design. 2011;7(4):228-30

Jayadeepa RM, Sharma S. Computational models for 5aR inhibitors for treatment of prostate cancer: Review of previous works and screening of natural inhibitors of 5aR2. Current Computer-Aided Drug Design. 2011;7(4):231-7

Speck-Planche A, Cordeiro MNDS, Guilarte-Montero L, Yera-Bueno R. Current computational approaches towards the rational design of new insecticidal agents. Current Computer-Aided Drug Design. 2011;7(4):304-14

Guha R, Wiggins GD, Wild DJ, Baik M-, Pierce And ME, Fox GC. Improving usability and accessibility of cheminformatics tools for chemists through cyberinfrastructure and education. In Silico Biology. 2011;11(1-2):41-60

Xu J, Wang L, Wang L, Shen X, Xu W. QSPR study of setschenow constants of organic compounds using MLR, ANN, and SVM analyses. Journal of Computational Chemistry. 2011;32(15):3241-52

Pereira F, Latino DARS, Aires-De-Sousa J. Estimation of mayr electrophilicity with a quantitative structure-property relationship approach using empirical and DFT descriptors. J Org Chem. 2011;76(22):9312-9

García J, Duchowicz PR, Rozas MF, Caram JA, Mirífico MV, Fernández FM, et al. A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases. J Mol Graph Model. 2011;31:10-9

Osoda T, Miyano S. 2D-QSAR for 450 types of amino acid induction peptides with a novel substructure pair descriptor having wider scope. Journal of Cheminformatics. 2011;3(11)

García I, Fall Y, García-Mera X, Prado-Prado F. Theoretical study of GSK-3a: Neural networks QSAR studies for the design of new inhibitors using 2D descriptors. Mol Divers. 2011;15(4):947-55

Li F, Li X, Liu X, Zhang L, You L, Zhao J, et al. Docking and 3D-QSAR studies on the ah receptor binding affinities of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Environ Toxicol Pharmacol. 2011;32(3):478-85

Hao M, Li Y, Wang Y, Zhang S. A classification study of human ß 3-adrenergic receptor agonists using BCUT descriptors. Mol Divers. 2011;15(4):877-87

Dejaegher B, Dhooghe L, Goodarzi M, Apers S, Pieters L, Vander Heyden Y. Classification models for neocryptolepine derivatives as inhibitors of the ß-haematin formation. Anal Chim Acta. 2011;705(1-2):98-110

Pérez-Garrido A, Helguera AM, Borges F, Cordeiro MNDS, Rivero V, Escudero AG. Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models. Journal of Chemical Information and Modeling. 2011;51(10):2746-59

Keshavarz MH, Ghanbarzadeh M. Simple method for reliable predicting flash points of unsaturated hydrocarbons. J Hazard Mater. 2011;193:335-41

Eslamimanesh A, Gharagheizi F, Mohammadi AH, Richon D. Phase equilibrium modeling of structure H clathrate hydrates of methane + water "insoluble" hydrocarbon promoter using QSPR molecular approach. J Chem Eng Data. 2011;56(10):3775-93

Gharagheizi F, Eslamimanesh A, Farjood F, Mohammadi AH, Richon D. Solubility parameters of nonelectrolyte organic compounds: Determination using quantitative structure-property relationship strategy. Industrial and Engineering Chemistry Research. 2011;50(19):11382-95

Dong X, Wang Y, Liu T, Wu P, Gao J, Xu J, et al. Flavonoids as vasorelaxant agents: Synthesis, biological evaluation and quantitative structure activities relationship (QSAR) studies. Molecules. 2011;16(10):8257-72

Minovski N, Jezierska-Mazzarello A, Vraèko M, Solmajer T. Investigation of 6-fluoroquinolones activity against mycobacterium tuberculosis using theoretical molecular descriptors: A case study. Central European Journal of Chemistry. 2011;9(5):855-66

Goyal RK, Singh G, Madan AK. Models for anti-tumor activity of bisphosphonates using refined topochemical descriptors. Naturwissenschaften. 2011;98(10):871-87

Bhhatarai B, Gramatica P. Prediction of aqueous solubility, vapor pressure and critical micelle concentration for aquatic partitioning of perfluorinated chemicals. Environmental Science and Technology. 2011;45(19):8120-8

Shahlaei M, Fassihi A, Saghaie L, Arkan E, Pourhossein A. A modeling study of aldehyde inhibitors of human cathepsin K using partial least squares method. Research in Pharmaceutical Sciences. 2011;6(2)

Tong J, Che T, Li Y, Wang P, Xu X, Chen Y. A descriptor of amino acids: Svrg and its application to peptide quantitative structure-activity relationship. SAR QSAR Environ Res. 2011;22(5-6):611-20

Ghasemi JB, Salahinejad M, Rofouei MK. Review of the quantitative structure-activity relationship modelling methods on estimation of formation constants of macrocyclic compounds with different guest molecules. Supramolecular Chemistry. 2011;23(9):615-31

Roy K, Das RN. On some novel extended topochemical atom (ETA) parameters for effective encoding of chemical information and modelling of fundamental physicochemical properties. SAR QSAR Environ Res. 2011;22(5-6):451-72

Combes RD. Challenges for computational structureactivity modelling for predicting chemical toxicity: Future improvements? Expert Opinion on Drug Metabolism and Toxicology. 2011;7(9):1129-40

Liew CY, Lim YC, Yap CW. Mixed learning algorithms and features ensemble in hepatotoxicity prediction. J Comput Aided Mol Des. 2011;25(9):855-71

Xiao X, Wang P, Chou K-. Cellular automata and its applications in protein bioinformatics. Current Protein and Peptide Science. 2011;12(6):508-19

Roy PP, Kovarich S, Gramatica P. QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles. Journal of Computational Chemistry. 2011;32(11):2386-96

Petrova T, Rasulev BF, Toropov AA, Leszczynska D, Leszczynski J. Improved model for fullerene C 60 solubility in organic solvents based on quantum-chemical and topological descriptors. Journal of Nanoparticle Research. 2011;13(8):3235-47

Andersson CR, Gustafsson MG, Strömbergsson H. Quantitative chemogenomics: Machine-learning models of protein-ligand interaction. Current Topics in Medicinal Chemistry. 2011;11(15):1978-93

Du H, Hu Z, Bazzoli A, Zhang Y. Prediction of inhibitory activity of epidermal growth factor receptor inhibitors using grid search-projection pursuit regression method. PLoS ONE. 2011;6(7)

Mercader AG, Duchowicz PR, Fernández FM, Castro EA. Advances in the replacement and enhanced replacement method in QSAR and QSPR theories. Journal of Chemical Information and Modeling. 2011;51(7):1575-81

Garkani-Nejad Z, Ahmadvand M. Simultaneous estimation of stability constants of mg, ba, ca, and sr complexes using a small subset of molecular descriptors. Journal of Coordination Chemistry. 2011;64(14):2466-79

Mu L, He H. Quantitative structure-property relations (QSPRs) for predicting the standard absolute entropy (S 298°K°) of gaseous organic compounds. Industrial and Engineering Chemistry Research. 2011;50(14):8764-72

Zhang S-, Tan Z-. Prediction of glass transition temperatures of polyarylates using a support vector machine model. Jiegou Huaxue. 2011;30(7):943-50

Mullen LMA, Duchowicz PR, Castro EA. QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents. Chemometrics Intellig Lab Syst. 2011;107(2):269-75

Roy K, Mitra I. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Combinatorial Chemistry and High Throughput Screening. 2011;14(6):450-74

Gozalbes R, Pineda-Lucena A. Small molecule databases and chemical descriptors useful in chemoinformatics: An overview. Combinatorial Chemistry and High Throughput Screening. 2011;14(6):548-58

Mallakpour S, Hatami M, Golmohammadi H. Theoretical study on modeling and prediction of optical rotation for biodegradable polymers containing a-amino acids using QSAR approaches. Journal of Molecular Modeling. 2011;17(7):1743-53

Duchowicz PR, Giraudo MA, Castro EA, Pomilio AB. Quantitative structure-property relationship analyses of aminograms in food: Hard cheeses. Chemometrics Intellig Lab Syst. 2011;107(2):384-90

Huo X, Li J, Gramatica P. Quantitative structure-activity relationship analysis of a novel series of chemicals antagonizing WT and MT AR. Chemometrics Intellig Lab Syst. 2011;107(2):283-9

Azar PA, Nekoei M, Siavash R, Ganjali MR, Zare K. A quantitative structure-retention relationship for the prediction of retention indices of the essential oils of ammoides atlantica. Journal of the Serbian Chemical Society. 2011;76(6):891-902

Kovarich S, Papa E, Gramatica P. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants. J Hazard Mater. 2011;190(1-3):106-12

Benfenati E, Toropov AA, Toropova AP, Manganaro A, Gonella Diaza R. CORAL software: QSAR for anticancer agents. Chemical Biology and Drug Design. 2011;77(6):471-6

Ghasemi JB, Rofouei MK, Salahinejad M. A quantitative structure-property relationships study of the stability constant of crown ethers by molecular modelling: New descriptors for lariat effect. Journal of Inclusion Phenomena and Macrocyclic Chemistry. 2011;70(1-2):37-47

Sharma BK, Pilania P, Singh P, Prabhakar YS. A QSAR study on 2-(4-methylpiperazin-1-yl)quinoxalines as human histamine H4 receptor ligands. Journal of Enzyme Inhibition and Medicinal Chemistry. 2011;26(3):412-21

Jalali-Heravi M, Mani-Varnosfaderani A. QSAR modelling of integrin antagonists using enhanced bayesian regularised genetic neural networks. SAR QSAR Environ Res. 2011;22(3-4):293-314

Rupp M, Körner R, Tetko IV. Predicting the pKa of small molecules. Combinatorial Chemistry and High Throughput Screening. 2011;14(5):307-27

Wang J, Hou T. Recent advances on aqueous solubility prediction. Combinatorial Chemistry and High Throughput Screening. 2011;14(5):328-38

Ghosh J, Lewitus DY, Chandra P, Joy A, Bushman J, Knight D, et al. Computational modeling of in vitro biological responses on polymethacrylate surfaces. Polymer. 2011;52(12):2650-60

Yap CW. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry. 2011;32(7):1466-74

Bhhatarai B, Gramatica P. Oral LD 50 toxicity modeling and prediction of per-and polyfluorinated chemicals on rat and mouse. Mol Divers. 2011;15(2):467-76

Garkani-Nejad Z, Poshteh-Shirani M. Modeling of 13C NMR chemical shifts of benzene derivatives using the RC-PC-ANN method: A comparative study of original molecular descriptors and multivariate image analysis descriptors. Canadian Journal of Chemistry. 2011;89(5):598-607

Shim J, MacKerell Jr. AD. Computational ligand-based rational design: Role of conformational sampling and force fields in model development. MedChemComm. 2011;2(5):356-70

The HP, González-Álvarez I, Bermejo M, Sanjuan VM, Centelles I, Garrigues TM, et al. In silico prediction of caco-2 cell permeability by a classification QSAR approach. Molecular Informatics. 2011;30(4):376-85

Bordás B, Bélai I, Komíves T. Theoretical molecular descriptors relevant to the uptake of persistent organic pollutants from soil by zucchini. A QSAR study. J Agric Food Chem. 2011;59(7):2863-9

Huang J, Fan X. Why QSAR fails: An empirical evaluation using conventional computational approach. Molecular Pharmaceutics. 2011;8(2):600-8

Mitra I, Saha A, Roy K. QSPR of antioxidant phenolic compounds using quantum chemical descriptors. Molecular Simulation. 2011;37(5):394-413

Du J, Xi L, Lei B, Liu H, Yao X. Structural requirements of isoquinolones as novel selective c-jun N-terminal kinase 1 inhibitors: 2D and 3D QSAR analyses. Chemical Biology and Drug Design. 2011;77(4):248-54

González-Díaz H, Prado-Prado F, García-Mera X, Alonso N, Abeijón P, Caamaño O, et al. MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from trichomonas gallinae. Journal of Proteome Research. 2011;10(4):1698-718

Casoni D, Sârbu C. Modeling of food preservatives chromatographic lipophilicity applying genetic algorithm and multiple linear regression. Revue Roumaine de Chimie. 2011;56(4):381-9

Mitra I, Saha A, Roy K. Chemometric QSAR modeling and in silico design of antioxidant no donor. Scientia Pharmaceutica. 2011;79(1):31-57

Noorizadeh H, Farmany A, Noorizadeh M. Quantitative structure-retention relationships analysis of retention index of essential oils. Quimica Nova. 2011;34(2):242-9

Bhhatarai B, Teetz W, Liu T, Öberg T, Jeliazkova N, Kochev N, et al. CADASTER QSPR models for predictions of melting and boiling points of perfluorinated chemicals. Molecular Informatics. 2011;30(2-3):189-204

Papa E, Kovarich S, Gramatica P. On the use of local and global qsprs for the prediction of physico-chemical properties of polybrominated diphenyl ethers. Molecular Informatics. 2011;30(2-3):232-40

Duardo-Sanchez A, Patlewicz G, González-Díaz H. Network topological indices from chem-bioinformatics to legal sciences and back. Current Bioinformatics. 2011;6(1):53-70

Riera-Fernández P, Munteanu CR, Pedreira-Souto N, Martín-Romalde R, Duardo-Sanchez A, González-Díaz H. Definition of markov-harary invariants and review of classic topological indices and databases in biology, parasitology, technology, and social-legal networks. Current Bioinformatics. 2011;6(1):94-121

Prado-Prado F, Escobar-Cubiella M, García-Mera X. Review of bioinformatics and QSAR studies of ß-secretase inhibitors. Current Bioinformatics. 2011;6(1):3-15

Speck-Planche A, Cordeiro MNDS. Application of bioinformatics for the search of novel anti-viral therapies: Rational design of anti-herpes agents. Current Bioinformatics. 2011;6(1):81-93

García I, Fall Y, Gómez G. Trends in bioinformatics and chemoinformatics of vitamin D analogs and their protein targets. Current Bioinformatics. 2011;6(1):16-24

Bhattacharjee B, Jayadeepa RM, Talambedu U, Banerjee S, Joshi J, Mole JP, et al. Complex network and gene ontology in pharmacology approaches: Mapping natural compounds on potential drug target colon cancer network. Current Bioinformatics. 2011;6(1):44-52

Dave K, Banerjee A. Bioinformatics analysis of functional relations between CNPs regions. Current Bioinformatics. 2011;6(1):122-8

Wan S-, Hu L-, Niu S, Wang K, Cai Y-, Lu W-, et al. Identification of multiple subcellular locations for proteins in budding yeast. Current Bioinformatics. 2011;6(1):71-80

Bouharis K, Souici ML, Messadi D. Retention indices for programmed-temperature gas chromatography of polycyclic aromatic hydrocarbons: A QSRR study. Asian Journal of Chemistry. 2011;23(3):1044-8

Xu J, Zhang H, Wang L, Xu W, Yi C, Liang H, et al. Quantitative structure-property relationship analysis for optical limiting of organic compounds based on genetic algorithm multivariate linear regression. Asian Journal of Chemistry. 2011;23(1):92-6

Fernandez M, Caballero J, Fernandez L, Sarai A. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol Divers. 2011;15(1):269-89

Rathke F, Hansen K, Brefeld U, Müller K-. Structrank: A new approach for ligand-based virtual screening. Journal of Chemical Information and Modeling. 2011;51(1):83-92

Duchowicz PR, Mirífico MV, Rozas MF, Caram JA, Fernández FM, Castro EA. Quantitative structure-spectral property relationships for functional groups of novel 1,2,5-thiadiazole compounds. Chemometrics Intellig Lab Syst. 2011;105(1):27-37

Gupta VK, Khani H, Ahmadi-Roudi B, Mirakhorli S, Fereyduni E, Agarwal S. Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks. Talanta. 2011;83(3):1014-22

Hilal R, Elroby SAK. A QSAR study for 2-(4-aminophenyl)benzothiazoles: Using DFT optimisation of geometry of molecules. Molecular Simulation. 2011;37(1):62-71

Fazeli A, Bagheri M, Ghaniyari-Benis S, Aslebagh R, Kamaloo E. Prediction of absolute entropy of ideal gas at 298 K of pure chemicals through GAMLR and FFNN. Energy Conversion and Management. 2011;52(1):630-4

Xu J, Zhang H, Wang L, Liang G, Wang L, Shen X. Artificial neural network-based QSPR study on absorption maxima of organic dyes for dye-sensitised solar cells. Molecular Simulation. 2011;37(1):1-10

Xu J, Wang L, Wang L, Zhang H, Xu W. Predicting infinite dilution activity coefficients of chlorinated organic compounds in aqueous solution based on three-dimensional WHIM and GETAWAY descriptors. Journal of Solution Chemistry. 2011;40(1):118-30

Bhhatarai B, Gramatica P. Modelling physico-chemical properties of (benzo)triazoles, and screening for environmental partitioning. Water Res. 2011;45(3):1463-71

Pan Y, Jiang JC, Wang R, Jiang JJ. Predicting the net heat of combustion of organic compounds from molecular structures based on ant colony optimization. J Loss Prev Process Ind. 2011;24(1):85-9

Zhang Y, Yang X, Sun C, Wang L. Quantitative structure-activity relationship of compounds binding to estrogen receptor ß based on heuristic method. Science China Chemistry. 2011;54(1):237-43

Carey AF, Wang G, Su C, Zwiebel LJ, Carlson JR. Odorant reception in the malaria mosquito Anopheles gambiae. Nature. 2010;464:66-7

Sushko I, Novotarskyi S, Körner R, Pandey AK, Cherkasov A, Li J, et al. Applicability domains for classification problems: Benchmarking of distance to models for ames mutagenicity set. Journal of Chemical Information and Modeling. 2010;50(12):2094-111

Goodarzi M, Chen T, Freitas MP. QSPR predictions of heat of fusion of organic compounds using bayesian regularized artificial neural networks. Chemometrics Intellig Lab Syst. 2010;104(2):260-4

Le-Thi-Thu H, Cardoso GC, Casañola-Martin GM, Marrero-Ponce Y, Puris A, Torrens F, et al. QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study. Chemometrics Intellig Lab Syst. 2010;104(2):249-59

Deeb O. Correlation ranking and stepwise regression procedures in principal components artificial neural networks modeling with application to predict toxic activity and human serum albumin binding affinity. Chemometrics Intellig Lab Syst. 2010;104(2):181-94

Chen C-, Liaw H-, Tsai Y-. Prediction of flash point of organosilicon compounds using quantitative structure property relationship approach. Industrial and Engineering Chemistry Research. 2010;49(24):12702-8

Pomilio AB, Giraudo MA, Duchowicz PR, Castro EA. QSPR analyses for aminograms in food: Citrus juices and concentrates. Food Chem. 2010;123(3):917-27

Garkani-Nejad Z, Saneie F. QSAR study of benzimidazole derivatives inhibition on escherichia coli methionine aminopeptidase. Bulletin of the Chemical Society of Ethiopia. 2010;24(3):317-25

Srivastava AK, Shukla N, Pathak VK. Quantitative structure activity relationship (QSAR) studies on a series of offtarget ion channel selective diltiazem sodium derivatives. Journal of the Indian Chemical Society. 2010;87(12):1517-23

Dutt R, Madan AK. Models for cannabinoid-1 receptor antagonistic activity of substituted 2-(3-pyrazolyl)-1,3,4-oxadiazoles. In Silico Biology. 2010;10(5-6):247-63

De Melo EB. Multivariate SAR/QSAR of 3-aryl-4-hydroxyquinolin-2(1H)-one derivatives as type i fatty acid synthase (FAS) inhibitors. Eur J Med Chem. 2010;45(12):5817-26

Srivastava AK, Srivastava A, Archana, Pathak VK. QSAR studies on potent Akt2 inhibitors of pyridopyrimidines series. Oxidation Commun. 2010;33(4):931-42

Sharma A, Phadni A, Sharma V, Khadikar PV. Estimation of 13C NMR chemical shift of carbinol carbon atoms by QSPR studies. Oxidation Commun. 2010;33(4):782-92

Kakoie Dinaki I, Zarrineh M. A quantitative structure-activity relationship study of the skin-irritant effect of thietanes. Monatshefte fur Chemie. 2010;141(12):1321-8

Tasnádi E, Moldovan C. Modeling the biological activity of 2-aryl-thiazole derivatives. Studia Universitatis Babes-Bolyai Chemia. 2010(4):77-81

Du J. Prediction on lower flammability limit temperature of organic compounds based on GA-BP neural network. Huagong Xuebao/CIESC Journal. 2010;61(12):3067-71

Shao L, Wu L, Fan X, Cheng Y. Consensus ranking approach to understanding the underlying mechanism with QSAR. Journal of Chemical Information and Modeling. 2010;50(11):1941-8

Li J, Gramatica P. The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders. Mol Divers. 2010;14(4):687-96

Tanabe K, Lucic B, Amic D, Kurita T, Kaihara M, Onodera N, et al. Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling. Mol Divers. 2010;14(4):789-802

Dutt R, Madan AK. Models for prediction of V600EBRAF and melanoma cells growth inhibitory activities of pyridoimidazolones. Arch Pharm (Weinheim). 2010;343(11-12):664-79

Ghavami R, Faham S. QSRR models for kováts' retention indices of a variety of volatile organic compounds on polar and apolar gc stationary phases using molecular connectivity indexes. Chromatographia. 2010;72(9-10):893-903

González-Díaz H, Romaris F, Duardo-Sanchez A, Pérez-Montoto LG, Prado-Prado F, Patlewicz G, et al. Predicting drugs and proteins in parasite infections with topological indices of complex networks: Theoretical backgrounds, applications and legal issues. Curr Pharm Des. 2010;16(24):2737-64

Munteanu CR, Fernández-Blanco E, Seoane JA, Izquierdo-Novo P, Rodríguez-Fernández JA, Prieto-González JM, et al. Drug discovery and design for complex diseases through QSAR computational methods. Curr Pharm Des. 2010;16(24):2640-55

Marrero-Ponce Y, Casañola-Martín GM, Khan MTH, Torrens F, Rescigno A, Abad C. Ligand-based computer-aided discovery of tyrosinase inhibitors. applications of the TOMOCOMD-CARDD method to the elucidation of new compounds. Curr Pharm Des. 2010;16(24):2601-24

González-Díaz H. QSAR and complex networks in pharmaceutical design, microbiology, parasitology, toxicology, cancer and neurosciences. Curr Pharm Des. 2010;16(24):2598-600

Srivastava AK, Pandey A. Exploring QSAR of 2,4-substituted 5-azolylthiopyrimidine analogues for screening against anti-influenza virus a. Oxidation Commun. 2010;33(3):620-30

Katritzky AR, Kuanar M, Slavov S, Hall CD, Karelson M, Kahn I, et al. Quantitative correlation of physical and chemical properties with chemical structure: Utility for prediction. Chem Rev. 2010;110(10):5714-89

Tasnadi E, Katona G, Diudea MV. Modeling of biologically active molecular structures. Studia Universitatis Babes-Bolyai Chemia. 2010;1:45-54

Lowe R, Glen RC, Mitchell JBO. Predicting phospholipidosis using machine learning. Molecular Pharmaceutics. 2010;7(5):1708-14

De Melo EB, Martins JPA, Jorge TCM, Friozi MC, Ferreira MMC. Multivariate QSAR study on the antimutagenic activity of flavonoids against 3-NFA on salmonella typhimurium TA98. Eur J Med Chem. 2010;45(10):4562-9

Onisor C, Posa M, Kevresan S, Kuhajda K, Sa¸rbu C. Estimation of chromatographic lipophilicity of bile acids and their derivatives by reversed-phase thin layer chromatography. Journal of Separation Science. 2010;33(20):3110-8

Shahlaei M, Sabet R, Ziari MB, Moeinifard B, Fassihi A, Karbakhsh R. QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components. Eur J Med Chem. 2010;45(10):4499-508

Li J, Gramatica P. QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs. SAR QSAR Environ Res. 2010;21(7):657-69

Mercader AG, Duchowicz PR, Fernández FM, Castro EA. Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories. Journal of Chemical Information and Modeling. 2010;50(9):1542-8

Goudarzi N, Goodarzi M. QSPR study of partition coefficient (Ko/w) of some organic compounds using radial basic function-partial least square (RBF-PLS). Journal of the Brazilian Chemical Society. 2010;21(9):1776-83

Chauhan P, Shakya M. Role of physicochemical properties in the estimation of skin permeability: In vitro data assessment by partial least-squares regression. SAR QSAR Environ Res. 2010;21(5-6):481-94

Yu X. Support vector machine-based QSPR for the prediction of glass transition temperatures of polymers. Fibers and Polymers. 2010;11(5):757-66

Hechinger M, Marquardt W. Targeted QSPR for the prediction of the laminar burning velocity of biofuels. Computers and Chemical Engineering. 2010;34(9):1507-14

Zhang Y-, Xia Z-, Qin L-, Liu S-. Prediction of blood-brain partitioning: A model based on molecular electronegativity distance vector descriptors. J Mol Graph Model. 2010;29(2):214-20

Ghavami R, Mohammad Sajadi S. Semi-empirical topological method for prediction of the relative retention time of polychlorinated biphenyl congeners on 18 different HR GC columns. Chromatographia. 2010;72(5-6):523-33

Gramatica P, Papa E, Luini M, Monti E, Gariboldi MB, Ravera M, et al. Antiproliferative pt(IV) complexes: Synthesis, biological activity, and quantitative structure-activity relationship modeling. Journal of Biological Inorganic Chemistry. 2010;15(7):1157-69

Noorizadeh H, Farmany A. QSRR models to predict retention indices of cyclic compounds of essential oils. Chromatographia. 2010;72(5-6):563-9

Vicente E, Duchowicz PR, Benítez D, Castro EA, Cerecetto H, González M, et al. Anti-T. cruzi activities and QSAR studies of 3-arylquinoxaline-2- carbonitrile di-N-oxides. Bioorganic and Medicinal Chemistry Letters. 2010;20(16):4831-5

Goodarzi M, Ortiz EV, Coelho LDS, Duchowicz PR. Linear and non-linear relationships mapping the henry's law parameters of organic pesticides. Atmos Environ. 2010;44(26):3179-86

Sârbu C, Casoni D, Kot-Wasik A, Wasik A, Namiesnik J. Modeling of chromatographic lipophilicity of food synthetic dyes estimated on different columns. Journal of Separation Science. 2010;33(15):2219-29

Liu W. Prediction of glass transition temperatures of aromatic heterocyclic polyimides using an ANN model. Polym Eng Sci. 2010;50(8):1547-57

García I, Fall Y, Gómez G. Using topological indices to predict anti-alzheimer and anti-parasitic GSK-3 inhibitors by multi-target QSAR in silico screening. Molecules. 2010;15(8):5408-22

Arkan E, Shahlaei M, Pourhossein A, Fakhri K, Fassihi A. Validated QSAR analysis of some diaryl substituted pyrazoles as CCR2 inhibitors by various linear and nonlinear multivariate chemometrics methods. Eur J Med Chem. 2010;45(8):3394-406

Fjodorova N, Vracko M, Novic M, Roncaglioni A, Benfenati E. New public QSAR model for carcinogenicity. Chemistry Central Journal. 2010;4(SUPPL. 1)

Su B-, Slien M-, Esposito EX, Hopnnger AJ, Tseng YJ. In silico binary classification QSAR models based on 4D-fingerprints and MOE descriptors for prediction of hERG blockage. Journal of Chemical Information and Modeling. 2010;50(7):1304-18

Xu J, Zhang H, Wang L, Liang G, Wang L, Shen X, et al. QSPR study of absorption maxima of organic dyes for dye-sensitized solar cells based on 3D descriptors. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy. 2010;76(2):239-47

Vicente E, Duchowicz PR, Ortiz EDV, Monge A, Castro EA. Exploring 3-arylquinoxaline-2-carbonitrile 1,4-di-N-oxides activities against neglected diseases with QSAR. Chemical Biology and Drug Design. 2010;76(1):59-69

Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V. Advances in computational methods to predict the biological activity of compounds. Expert Opinion on Drug Discovery. 2010;5(7):633-54

Wei Y, Xi L, Chen D, Wu X, Liu H, Yao X. Extraction, separation and quantitative structure-retention relationship modeling of essential oils in three herbs. Journal of Separation Science. 2010;33(13):1980-90

Goodarzi M, Duchowicz PR, Freitas MP, Fernández FM. Prediction of the hildebrand parameter of various solvents using linear and nonlinear approaches. Fluid Phase Equilib. 2010;293(2):130-6

Atabati M, Zarei K, Borhani A. Predicting infinite dilution activity coefficients of hydrocarbons in water using ant colony optimization. Fluid Phase Equilib. 2010;293(2):219-24

Dehmer MM, Barbarini NN, Varmuza KK, Graber AA. Novel topological descriptors for analyzing biological networks. BMC Structural Biology. 2010;10

Talevi A, Bellera CL, Castro EA, Bruno-Blanch LE. Optimal partition of datasets of QSPR studies: A sampling problem. Match. 2010;63(3):585-99

Leszczynska D, Leszczynski J. In: Current trends and challenges of modeling and experimenting on toxicity of nanoparticles. AIP conference proceedings; ; 2010. p. 23-8

Da Silva Veras L, Arakawa M, Funatsu K, Takahata Y. 2D and 3D QSAR studies of the receptor binding affinity of progestins. Journal of the Brazilian Chemical Society. 2010;21(5):872-81

Maldonado AG, Rothenberg G. Predictive modeling in homogeneous catalysis: A tutorial. Chem Soc Rev. 2010;39(6):1891-902

Rasulev B, Kusic H, Leszczynska D, Leszczynski J, Koprivanac N. QSAR modeling of acute toxicity on mammals caused by aromatic compounds: The case study using oral LD 50 for rats. Journal of Environmental Monitoring. 2010;12(5):1037-44

Li J, Gramatica P. Classification and virtual screening of androgen receptor antagonists. Journal of Chemical Information and Modeling. 2010;50(5):861-74

Papa E, Gramatica P. QSPR as a support for the EU REACH regulation and rational design of environmentally safer chemicals: PBT identification from molecular structure. Green Chem. 2010;12(5):836-43

Papa E, Kovarich S, Gramatica P. QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants. Chem Res Toxicol. 2010;23(5):946-54

Srivastava AK, Pandey A, Nath A, Jaiswal M, Pathak VK. Quantitative structure activity relationship studies on a series of xanthone derivatives as a-glucosidase inhibitors. Oxidation Commun. 2010;33(1):195-204

Srivastava AK, Srivastava A, Jaiswal M, Pathak VK, Pandey A. QSAR modelling of chek-1 kinase inhibitory activity of C6-substituted indolylquinolinone analogs. Oxidation Commun. 2010;33(1):205-15

Khan MTH. Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr Drug Metab. 2010;11(4):285-95

Pérez-Garrido A, Helguera AM, Rodríguez FG, Cordeiro MNDS. QSAR models to predict mutagenicity of acrylates, methacrylates and a,ß-unsaturated carbonyl compounds. Dental Materials. 2010;26(5):397-415

Pomilio AB, Duchowicz PR, Giraudo MA, Castro EA. Amino acid profiles and quantitative structure-property relationships for malts and beers. Food Res Int. 2010;43(4):965-71

Onisor C, Palage M, Sârbu C. Modeling of chromatographic lipophilicity indices of quaternary ammonium and nitrone derivatives and their thiazolic salts using molecular descriptors. Anal Lett. 2010;43(7):1132-48

González-Díaz H, Duardo-Sanchez A, Ubeira FM, Prado-Prado F, Pérez-Montoto LG, Concu R, et al. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. Curr Drug Metab. 2010;11(4):379-406

Mercader AG, Pomilio AB. QSAR study of flavonoids and biflavonoids as influenza H1N1 virus neuraminidase inhibitors. Eur J Med Chem. 2010;45(5):1724-30

Fjodorova N, Vracko M, Jezierska A, Novic M. Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals. SAR QSAR Environ Res. 2010;21(1-2):57-75

Sharma BK, Singh P, Sarbhai K, Prabhakar YS. A quantitative structure-activity relationship study on serotonin 5-HT6 receptor ligands: Indolyl and piperidinyl sulphonamides. SAR QSAR Environ Res. 2010;21(3):369-88

Helguera AM, Pérez-Machado G, Cordeiro MNDS, Combes RD. Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach. SAR QSAR Environ Res. 2010;21(3):277-304

Shahlaei M, Fassihi A, Saghaie L. Application of PC-ANN and PC-LS-SVM in QSAR of CCR1 antagonist compounds: A comparative study. Eur J Med Chem. 2010;45(4):1572-82

Goodarzi M, Freitas MP, Wu CH, Duchowicz PR. pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression. Chemometrics Intellig Lab Syst. 2010;101(2):102-9

Garkani-Nejad Z. Quantitative structure-retention relationship study of some phenol derivatives in gas chromatography. J Chromatogr Sci. 2010;48(4):317-23

Fatemi MH, Malekzadeh H. Prediction of log(IGC50)-1 for benzene derivatives to ciliate tetrahymena pyriformis from their molecular descriptors. Bull Chem Soc Jpn. 2010;83(3):233-45

Kramer C, Beck B, Clark T. Insolubility classification with accurate prediction probabilities using a metaClassifier. Journal of Chemical Information and Modeling. 2010;50(3):404-14

Helguera AM, Rodríguez-Borges JE, Caamaño O, García-Mera X, González MP, Cordeiro MNDS. Design, synthesis, and evaluation of antineoplastic activity of novel carbocyclic nucleosides. Molecular Informatics. 2010;29(3):213-31

Bhhatarai B, Gramatica P. Per-and polyfluoro toxicity (LC50 inhalation) study in rat and mouse using QSAR modeling. Chem Res Toxicol. 2010;23(3):528-39

Pan Y, Jiang J, Ding X, Wang R, Jiang J. Prediction of flammability characteristics of pure hydrocarbons from molecular structures. AIChE J. 2010;56(3):690-701

Planche AS, Scotti MT, De Emerenciano VP, López AG, Pérez EM, Uriarte E. Designing novel antitrypanosomal agents from a mixed graph-theoretical substructural approach. Journal of Computational Chemistry. 2010;31(4):882-94

Garkani-Nejad Z, Rashidi-Nodeh H. Comparison of conventional artificial neural network and wavelet neural network in modeling the half-wave potential of aldehydes and ketones. Electrochim Acta. 2010;55(8):2597-605

D'Archivio AA, Maggi MA, Ruggieri F. Multiple-column RP-HPLC retention modelling based on solvatochromic or theoretical solute descriptors. Journal of Separation Science. 2010;33(2):155-66

Tan N-, Li P, Rao H-, Li Z-, Li X-. Prediction of the acute toxicity of chemical compounds to the fathead minnow by machine learning approaches. Chemometrics Intellig Lab Syst. 2010;100(1):66-73

Patel A, Karthikeyan C, Moorthy NSHN, Trivedi P. QSAR study on hetaryl imidazoles: A novel dual inhibitor of VEGF receptors I and II. Medicinal Chemistry. 2010;6(1):24-9

Xu J, Wang L, Zhang H, Yi C, Xu W. Accurate quantitative structure-property relationship analysis for prediction of nematic transition temperatures in thermotropic liquid crystals. Molecular Simulation. 2010;36(1):26-34

Haddad R, Khan R, Takahashi YK, Mori K, Harel D, Sobel N. A metric for odorant comparison. Nature Methods. 2008;5(5):425-9

Ballabio D, Manganaro A, Consonni V, Mauri A, Todeschini R. Introduction to MOLE db - on-line molecular descriptors database. Match. 2009;62(1):199-207

Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chem Res Toxicol. 2009;22(12):1913-21

Duchowicz PR, Goodarzi M, Ocsachoque MA, Romanelli GP, Ortiz EdV, Autino JC, et al. QSAR analysis on spodoptera litura antifeedant activities for flavone derivatives. Sci Total Environ. 2009;408(2):277-85

Garro Martinez JC, Duchowicz PR, Estrada MR, Zamarbide GN, Castro E. Anticonvulsant activity of ringed enaminones: A QSAR study. QSAR and Combinatorial Science. 2009;28(11-12):1376-85

Soto AJ, Cecchini RL, Vazquez GE, Ponzoni I. Multi-objective feature selection in QSAR using a machine learning approach. QSAR and Combinatorial Science. 2009;28(11-12):1509-23

Ibezim EC, Duchowicz PR, Ibezim NE, Mullen LMA, Onyishi IV, Brown SA, et al. Computer-aided linear modeling employing QSAR for drug discovery. Scientific Research and Essays. 2009;4(13):1559-64

Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V. A practical overview of quantitative structure-activity relationship. EXCLI Journal. 2009;8:74-88

Dutt R, Dureja H, Madan AK. Models for prediction of anti- HIV-1 activity of 5-alkyl-2-alkylamino-6-(2, 6-difluorophenylalkyl)-3,4-dihydropyrimidin-4(3H)-ones using random forest, decision tree and moving average analysis. Journal of Computational Methods in Sciences and Engineering. 2009;9(3):95-112

Khan AKR, Sahu VK, Singh RK, Khan SA. Comparative QSTR study of saturated alcohols based on topological, constitutional, geometrical, and getaway descriptors. Medicinal Chemistry Research. 2009;18(9):770-81

Koter S, Gilewicz-Lukasik B, Nowaczyk A, Nowaczyk J. Application of molecular descriptors to the prediction of retention in organic solvent nanofiltration. Pol J Chem. 2009;83(12):2137-52

Roy K, Ghosh G. QSTR with extended topochemical atom (ETA) indices. 13. modelling of hERG K+ channel blocking activity of diverse functional drugs using different chemometric tools. Molecular Simulation. 2009;35(15):1256-68

Gharagheizi F, Sattari M. Estimation of molecular diffusivity of pure chemicals in water: A quantitative structure-property relationship study. SAR QSAR Environ Res. 2009;20(3-4):267-85

Nowaczyk A, Kulig K, Malawska B. 1-(3-(4-arylpiperazin-1-yl)-propyl)-pyrrolidin-2-one derivatives as a1-adrenoceptor antagonists: A QSAR study. QSAR and Combinatorial Science. 2009;28(9):979-88

Puzyn T, Leszczynska D, Leszczynski J. Toward the development of "nano-QSARs": Advances and challenges. Small. 2009;5(22):2494-509

Munteanu CR, Vázquez JM, Dorado J, Sierra AP, Sánchez-González Á, Prado-Prado FJ, et al. Complex network spectral moments for ATCUN motif DNA cleavage: First predictive study on proteins of human pathogen parasites. Journal of Proteome Research. 2009;8(11):5219-28

Neely BJ, Madihally SV, Robinson Jr. RL, Gasem KAM. Nonlinear quantitative structure-property relationship modeling of skin permeation coefficient. J Pharm Sci. 2009;98(11):4069-84

Pérez-Montoto LG, Santana L, González-Díaz H. Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories. Eur J Med Chem. 2009;44(11):4461-9

Roy K, Mitra I. Advances in quantitative structureactivity relationship models of antioxidants. Expert Opinion on Drug Discovery. 2009;4(11):1157-75

Lei B, Li J, Lu J, Du J, Liu H, Yao X. Rational prediction of the herbicidal activities of novel protoporphyrinogen oxidase inhibitors by quantitative structure-activity relationship model based on docking-guided active conformation. J Agric Food Chem. 2009;57(20):9593-8

Klon AE. Computational models for central nervous system penetration. Current Computer-Aided Drug Design. 2009;5(2):71-89

Gharagheizi F, Sattari M. Prediction of the ?(UCST) of polymer solutions: A quantitative structure-property relationship study. Industrial and Engineering Chemistry Research. 2009;48(19):9054-60

Done R, Mandrila G, Tarko L. QSAR study concerning toxicity and threshold limit value VL8 of chlorine containing compounds. Rev Chim. 2009;60(10):992-5

Dong X, Jiang C, Hu H, Yan J, Chen J, Hu Y. QSAR study of Akt/protein kinase B (PKB) inhibitors using support vector machine. Eur J Med Chem. 2009;44(10):4090-7

Liang G, Yang L, Kang L, Mei H, Li Z. Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides. Amino Acids. 2009;37(4):583-91

D'Archivio AA, Incani A, Mazzeo P, Ruggieri F. Adsorption of s-triazines onto polybenzimidazole: A quantitative structure-property relationship investigation. Anal Chim Acta. 2009;650(2):175-82

Wang J, Hou T. Chapter 5 recent advances on in silico ADME modeling [Internet]; 2009

Ghavami R, Sadeghi F. QSRR-based evaluating and predicting of the relative retention time of polychlorinated biphenyl congeners on 18 different high resolution GC columns. Chromatographia. 2009;70(5-6):851-68

Garkani-Nejad Z. Use of self-training artificial neural networks in a QSRR study of a diverse set of organic compounds. Chromatographia. 2009;70(5-6):869-74

Kuz'min VE, Muratov EN, Artemenko AG, Varlamova EV, Gorb L, Wang J, et al. Consensus QSAR modeling of phosphor-containing chiral AChE inhibitors. QSAR and Combinatorial Science. 2009;28(6-7):664-77

Vicente E, Duchowicz PR, Castro EA, Monge A. QSAR analysis for quinoxaline-2-carboxylate 1,4-di-N-oxides as anti-mycobacterial agents. J Mol Graph Model. 2009;28(1):28-36

Mracec M, Gruia A, Borota A, Ramona Curpan RAD, Ostopovici LH, Mracec M. QSAR study of a series of quinoline derivatives active on the alpha2 adrenergic receptor subtypes. Revue Roumaine de Chimie. 2009;54(8):651-7

Artemenko AG, Kuz'Min VE, Muratov EN, Polishchuk PG, Borisyuk IY, Golovenko NY. Influence of the structure of substituted benzodiazepines on their pharmacokinetic properties. Pharmaceutical Chemistry Journal. 2009;43(8):454-62

Bélai I, Oros G, Bordás B. Quantitative structure-retention relationship and 3D molecular modeling studies of the unusual chromatographic behavior of triphenylmethane derivatives in RPTLC systems. Journal of Planar Chromatography - Modern TLC. 2009;22(4):255-63

Zhang X, Ding L, Sun Z, Song L, Sun T. Study on quantitative structure-retention relationships for hydrocarbons in FCC gasoline. Chromatographia. 2009;70(3-4):511-8

Dehmer M, Varmuza K, Borgert S, Emmert-Streib F. On entropy-based molecular descriptors: Statistical analysis of real and synthetic chemical structures. Journal of Chemical Information and Modeling. 2009;49(7):1655-63

Pérez-Montoto LG, Dea-Ayuela MA, Prado-Prado FJ, Bolas-Fernández F, Ubeira FM, González-Díaz H. Study of peptide fingerprints of parasite proteins and drug-DNA interactions with markov-mean-energy invariants of biopolymer molecular-dynamic lattice networks. Polymer. 2009;50(15):3857-70

Concu R, Podda G, Uriarte E, González-Díaz H. Computational chemistry study of 3D-structure-function relationships for enzymes based on markov models for protein electrostatic, HINT, and van der waals potentials. Journal of Computational Chemistry. 2009;30(9):1510-20

Stevanovic D, Ilic A, Onisor C, Diudea MV. LEL - A newly designed molecular descriptor. Acta Chimica Slovenica. 2009;56(2):410-7

Duchowicz PR, Marrugo H JJ, Ortiz EV, Castro EA, Vivas-Reyes R. QSAR study for the fish toxicity of benzene derivatives. Journal of the Argentine Chemical Society. 2009;97(2):116-27

Yuan H, Huang J, Cao C. Prediction of skin sensitization with a particle swarm optimized support vector machine. International Journal of Molecular Sciences. 2009;10(7):3237-54

Lei B, Xi L, Li J, Liu H, Yao X. Global, local and novel consensus quantitative structure-activity relationship studies of 4-(phenylaminomethylene) isoquinoline-1, 3 (2H, 4H)-diones as potent inhibitors of the cyclin-dependent kinase 4. Anal Chim Acta. 2009;644(1-2):17-24

Zarei K, Atabati M. QSAR study of anti-HIV activities against HIV-1 and some of their mutant strains for a group of HEPT derivatives. J Chin Chem Soc. 2009;56(1):206-13

Sun M, Zheng Y, Wei H, Chen J, Cai J, Jin M. Enhanced replacement method-based quantitative structure-activity relationship modeling and support vector machine classification of 4-anilino-3-quinolinecarbonitriles as src kinase inhibitors. QSAR and Combinatorial Science. 2009;28(3):312-24

Duchowicz PR, Ocsachoque MA. Quantitative structure-toxicity models for heterogeneous aliphatic compounds. QSAR and Combinatorial Science. 2009;28(3):281-95

Duchowicz PR, Castro EA. QSPR studies on aqueous solubilities of drug-like compounds. International Journal of Molecular Sciences. 2009;10(6):2558-77

Sun M, Chen J, Wei H, Yin S, Yang Y, Ji M. Quantitative structure-activity relationship and classification analysis of diaryl ureas against vascular endothelial growth factor receptor-2 kinase using linear and non-linear models. Chemical Biology and Drug Design. 2009;73(6):644-54

Kornhuber J, Terfloth L, Bleich S, Wiltfang J, Rupprecht R. Molecular properties of psychopharmacological drugs determining non-competitive inhibition of 5-HT3A receptors. Eur J Med Chem. 2009;44(6):2667-72

Spafiu F, Mischie A, Ionita P, Beteringhe A, Constantinescu T, Balaban AT. New alternatives for estimating the octanol/water partition coefficient and water solubility for volatile organic compounds using GLC data (kovàts retention indices). Arkivoc. 2009;2009(10):174-94

Kusic H, Rasulev B, Leszczynska D, Leszczynski J, Koprivanac N. Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: A QSAR study. Chemosphere. 2009;75(8):1128-34

Li S, Xi L, Wang C, Li J, Lei B, Liu H, et al. A novel method for protein-ligand binding affinity prediction and the related descriptors exploration. Journal of Computational Chemistry. 2009;30(6):900-9

Podunavac-Kuzmanovic SO, Cvetkovic DD, Barna DJ. QSAR analysis of 2-amino or 2-methyl-1-substituted benzimidazoles against pseudomonas aeruginosa. International Journal of Molecular Sciences. 2009;10(4):1670-82

Oliferenko PV, Oliferenko AA, Poda G, Palyulin VA, Zeflrov NS, Katritzky AR. New developments in hydrogen bonding acidity and basicity of small organic molecules for the prediction of physical and ADMET properties. part 2. the universal solvation equation. Journal of Chemical Information and Modeling. 2009;49(3):634-46

Li F, Chen J, Wang Z, Li J, Qiao X. Determination and prediction of xenoestrogens by recombinant yeast-based assay and QSAR. Chemosphere. 2009;74(9):1152-7

Xu J, Xiong Q, Chen B, Wang L, Liu L, Xu W. Modeling the relative fluorescence intensity ratio of eu(III) complex in different solvents based on QSPR method. J Fluoresc. 2009;19(2):203-9

Mannhold R, Poda GI, Ostermann C, Tetko IV. Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds. J Pharm Sci. 2009;98(3):861-93

Mihaleva VV, Verhoeven HA, de Vos RCH, Hall RD, van Ham RCHJ. Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of kovats retention index. Bioinformatics. 2009;25(6):787-94

Singh RK, Khan AKR, Sahu VK, Singh PP. Comparative QSTR study of a series of alcohol derivatives against tetrahymena pyriformis. International Journal of Quantum Chemistry. 2009;109(2):185-95

Li J, Du J, Xi L, Liu H, Yao X, Liu M. Validated quantitative structure-activity relationship analysis of a series of 2-aminothiazole based p56Lck inhibitors. Anal Chim Acta. 2009;631(1):29-39

Czodrowski P, Kriegl JM, Scheuerer S, Fox T. Computational approaches to predict drug metabolism. Expert Opinion on Drug Metabolism and Toxicology. 2009;5(1):15-27

Chen X, Liang YZ, Yuan DL, Xu QS. A modified uncorrelated linear discriminant analysis model coupled with recursive feature elimination for the prediction of bioactivity. SAR QSAR Environ Res. 2009;20(1-2):1-26

González-Díaz H, Cabrera-Pérez MA, Agüero-Chapín G, Cruz-Monteagudo M, Castañeda-Cancio N, del Río MA, et al. Multi-target QSPR assemble of a complex network for the distribution of chemicals to biphasic systems and biological tissues. Chemometrics Intellig Lab Syst. 2008;94(2):160-5

Duchowicz PR, Castro EA. Partial order theory applied to QSPR-QSAR studies. Combinatorial Chemistry and High Throughput Screening. 2008;11(10):783-93

Helguera AM, Combes RD, Pérez González M, Cordeiro MNDS. Applications of 2D descriptors in drug design: A DRAGON tale. Current Topics in Medicinal Chemistry. 2008;8(18):1628-55

Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery. Current Topics in Medicinal Chemistry. 2008;8(18):1555-72

Pérez González M, Terán C, Saíaz-Urra L, Teijeira M. Variables selection methods in QSAR: An overview. Current Topics in Medicinal Chemistry. 2008;8(18):1606-27

Cruz-Monteagudo M, Munteanu CR, Borges F, Cordeiro MNDS, Uriarte E, Chou K-, et al. Stochastic molecular descriptors for polymers. 4. study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case. Polymer. 2008;49(25):5575-87

Korichi M, Gerbaud V, Talou T, Floquet P, Meniai A, Nacef S, Computer-aided aroma design. II. Quantitative structure–odour relationship, Chemical Engineering and Processing 2008;47:1912–1925

Cholakov GS, Stateva RP, Brauner N, Shacham M. Estimation of properties of homologous series with targeted quantitative structure-property relationships. J Chem Eng Data. 2008;53(11):2510-20

Sharma S, Prabhakar YS, Singh P, Sharma BK. QSAR study about ATP-sensitive potassium channel activation of cromakalim analogues using CP-MLR approach. Eur J Med Chem. 2008;43(11):2354-60

Agüero-Chapín G, Antunes A, Ubeira FM, Chou K-, González-Díaz H. Comparative study of topological indices of macro/supramolecular RNA complex networks. Journal of Chemical Information and Modeling. 2008;48(11):2265-77

Ruiz IL, Gómez-Nieto MÁ. A tool for the calculation of molecular descriptors in the development of QSAR models [Internet]; 2008

Papa E, Gramatica P. Externally validated QSPR modelling of VOC tropospheric oxidation by NO3 radicals. SAR QSAR Environ Res. 2008;19(7-8):655-68

Konovalov DA, Llewellyn LE, Heyden YV, Coomans D. Robust cross-validation of linear regression QSAR models. Journal of Chemical Information and Modeling. 2008;48(10):2081-94

Atabati M, Zarei K. Prediction of GC retention indexes for insect-produced methyl-substituted alkanes using a wavelet neural network. J Chin Chem Soc. 2008;55(4):732-9

Duchowicz PR, Talevi A, Bruno-Blanch LE, Castro EA. New QSPR study for the prediction of aqueous solubility of drug-like compounds. Bioorganic and Medicinal Chemistry. 2008;16(17):7944-55

Castillo-Garit JA, Marrero-Ponce Y, Escobar J, Torrens F, Rotondo R. A novel approach to predict aquatic toxicity from molecular structure. Chemosphere. 2008;73(3):415-27

Duchowicz PR, Vitale MG, Castro EA, Autino JC, Romanelli GP, Bennardi DO. QSAR modeling of the interaction of flavonoids with GABA(A) receptor. Eur J Med Chem. 2008;43(8):1593-602

Mercader AG, Duchowicz PR, Fernández FM, Castro EA, Bennardi DO, Autino JC, et al. QSAR prediction of inhibition of aldose reductase for flavonoids. Bioorganic and Medicinal Chemistry. 2008;16(15):7470-6

Zhang L, Zhu H, Oprea TI, Golbraikh A, Tropsha A. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm Res. 2008;25(8):1902-14

Gharagheizi F, Mehrpooya M. Prediction of some important physical properties of sulfur compounds using quantitative structure-properties relationships. Mol Divers. 2008;12(3-4):143-55

Kahrs O, Brauner N, Cholakov GS, Stateva RP, Marquardt W, Shacham M. Analysis and refinement of the targeted QSPR method. Computers and Chemical Engineering. 2008;32(7):1397-410

Xu J, Liang H, Chen B, Xu W, Shen X, Liu H. Linear and nonlinear QSPR models to predict refractive indices of polymers from cyclic dimer structures. Chemometrics Intellig Lab Syst. 2008;92(2):152-6

Farkas O, Zenkevich IG, Stout F, Kalivas JH, Héberger K. Prediction of retention indices for identification of fatty acid methyl esters. Journal of Chromatography A. 2008;1198-1199(1-2):188-95

Konoz E, Golmohammadi H. Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network. Anal Chim Acta. 2008;619(2):157-64

Hong H, Xie Q, Ge W, Qian F, Fang H, Shi L, et al. Mold 2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. Journal of Chemical Information and Modeling. 2008;48(7):1337-44

Ji L, Wang X, Luo S, Qin L, Yang X, Liu S, et al. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network. Science in China, Series B: Chemistry. 2008;51(7):677-83

Lei B, Li J, Liu H, Yao X. Accurate prediction of aquatic toxicity of aromatic compounds based on genetic algorithm and least squares support vector machines. QSAR and Combinatorial Science. 2008;27(7):850-65

Estrada E. Quantum-chemical foundations of the topological substructural molecular design. Journal of Physical Chemistry A. 2008;112(23):5208-17

Prado-Prado FJ, González-Díaz H, de la Vega OM, Ubeira FM, Chou K-. Unified QSAR approach to antimicrobials. part 3: First multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorganic and Medicinal Chemistry. 2008;16(11):5871-80

Tsygankova IG. Variable selection in QSAR models for drug design. Current Computer-Aided Drug Design. 2008;4(2):132-42

Gharagheizi F, Alamdari RF. Prediction of flash point temperature of pure components using a quantitative structure-property relationship model. QSAR and Combinatorial Science. 2008;27(6):679-83

Shen J, Du Y, Zhao Y, Liu G, Tang Y. In silico prediction of blood-brain partitioning using a chemometric method called genetic algorithm based variable selection. QSAR and Combinatorial Science. 2008;27(6):704-17

Gupta AK, Gupta RA, Soni LK, Kaskhedikar SG. Exploration of physicochemical properties and molecular modelling studies of 2-sulfonyl-phenyl-3-phenyl-indole analogs as cyclooxygenase-2 inhibitors. Eur J Med Chem. 2008;43(6):1297-303

Wang XS, Tang H, Golbraikh A, Tropsha A. Combinatorial QSAR modeling of specificity and subtype selectivity of ligands binding to serotonin receptors 5HT1E and 5HT1F. Journal of Chemical Information and Modeling. 2008;48(5):997-1013

Rasulev BF, Toropov AA, Hamme II AT, Leszczynski J. Multiple linear regression analysis and optimal descriptors: Predicting the cholesteryl ester transfer protein inhibition activity. QSAR and Combinatorial Science. 2008;27(5):595-606

Fang Y, Feng Y, Li M. Optimal QSAR analysis of the carcinogenic activity of aromatic and heteroaromatic amines. QSAR and Combinatorial Science. 2008;27(5):543-54

Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R. Estimation of ADME properties in drug discovery: Predicting caco-2 cell permeability using atom-based stochastic and non-stochastic linear indices. J Pharm Sci. 2008;97(5):1946-76

Yan D, Jiang X, Xu S, Wang L, Bian Y, Yu G. Quantitative structure-toxicity relationship study of lethal concentration to tadpole (bufo vulgaris formosus) for organophosphorous pesticides. Chemosphere. 2008;71(10):1809-15

Brauner N, Cholakov GS, Kahrs O, Stateva RP, Shacham M. Linear QSPRs for predicting pure compound properties in homologous series. AIChE J. 2008;54(4):978-90

Schwaighofer A, Schroeter T, Mika S, Hansen K, Ter Laak A, Lienau P, et al. A probabilistic approach to classifying metabolic stability. Journal of Chemical Information and Modeling. 2008;48(4):785-96

Estrada E. How the parts organize in the whole? A top-down view of molecular descriptors and properties for QSAR and drug design. Mini-Reviews in Medicinal Chemistry. 2008;8(3):213-21

Ramírez-Galicia G, Garduño-Juárez R, Deeb O, Hemmateenejad B. MLR-ANN and RTO approach to µ-opioid receptor-binding affinity. pooling data from different sources. Chemical Biology and Drug Design. 2008;71(3):260-70

Cruz-Monteagudo M, Cordeiro MNDS, Borges F. Computational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity. Journal of Computational Chemistry. 2008;29(4):533-49

Duchowicz PR, Castro EA, Fernández FM. Application of a novel ranking approach in QSPR-QSAR. Journal of Mathematical Chemistry. 2008;43(2):620-36

Konovalov DA, Sim N, Deconinck E, Heyden YV, Coomans D. Statistical confidence for variable selection in QSAR models via monte carlo cross-validation. Journal of Chemical Information and Modeling. 2008;48(2):370-83

Dastmalchi S, Hamzeh-Mivehroud M, Ghafourian T, Hamzeiy H. Molecular modeling of histamine H3 receptor and QSAR studies on arylbenzofuran derived H3 antagonists. J Mol Graph Model. 2008;26(5):834-44

Singh J, Singh S, Mishra R, Khadikar PV, Supuran CT, Clare BW, et al. Estimation of human carbonic anhydrase II inhibition using topological indices and their combination with quantum-theoretical descriptors. Medicinal Chemistry. 2008;4(1):30-66

Hughes LD, Palmer DS, Nigsch F, Mitchell JBO. Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and log P. Journal of Chemical Information and Modeling. 2008;48(1):220-32

Moustafa NE. Prediction of GC retention times of complex petroleum fractions based on quantitative structure-retention relationships. Chromatographia. 2008;67(1-2):85-91

Kim JH, Gramatica P, Kim MG, Kim D, Tratnyek PG. QSAR modelling of water quality indices of alkylphenol pollutants. SAR QSAR Environ Res. 2007;18(7-8):729-43

Tong JB, Zhang S-. A new 3D-descriptor of amino acids and its application in quantitative structure activity relationship of peptide drugs. Wuli Huaxue Xuebao/ Acta Physico - Chimica Sinica. 2007;23(1):37-43

Baert B, Deconinck E, Van Gele M, Slodicka M, Stoppie P, Bodé S, et al. Transdermal penetration behaviour of drugs: CART-clustering, QSPR and selection of model compounds. Bioorganic and Medicinal Chemistry. 2007;15(22):6943-55

Shacham M, Kahrs O, Cholakov GS, Stateva RP, Marquardt W, Brauner N. The role of the dominant descriptor in targeted quantitative structure property relationships. Chemical Engineering Science. 2007;62(22):6222-33

Duchowicz PR, González MP, Helguera AM, Natália Dias Soeiro Cordeiro M, Castro EA. Application of the replacement method as novel variable selection in QSPR. 2. soil sorption coefficients. Chemometrics Intellig Lab Syst. 2007;88(2):197-203

Liu H, Gramatica P. QSAR study of selective ligands for the thyroid hormone receptor ß. Bioorganic and Medicinal Chemistry. 2007;15(15):5251-61

Ramírez-Galicia G, Garduño-Juarez R, Hemmateenejad B, Deeb O, Estrada-Soto S. QSAR study on the relaxant agents from some mexican medicinal plants and synthetic related organic compounds. Chemical Biology and Drug Design. 2007;70(2):143-53

Zhao M, Li Z, Wu Y, Tang Y-, Wang C, Zhang Z, et al. Studies on log P, retention time and QSAR of 2-substituted phenylnitronyl nitroxides as free radical scavengers. Eur J Med Chem. 2007;42(7):955-65

Arab Chamjangali M, Beglari M, Bagherian G. Prediction of cytotoxicity data (CC50) of anti-HIV 5-pheny-l-phenylamino-1H-imidazole derivatives by artificial neural network trained with levenberg-marquardt algorithm. J Mol Graph Model. 2007;26(1):360-7

Liu H, Papa E, Walker JD, Gramatica P. In silico screening of estrogen-like chemicals based on different nonlinear classification models. J Mol Graph Model. 2007;26(1):135-44

Ramírez-Galicia G, Garduño-Juárez R, Hemmateenejad B, Deeb O, Deciga-Campos M, Moctezuma-Eugenio JC. QSAR study on the antinociceptive activity of some morphinans. Chemical Biology and Drug Design. 2007;70(1):53-64

Kaliszan R. QSRR: Quantitative structure-(chromatographic) retention relationships. Chem Rev. 2007;107(7):3212-46

Duchowicz PR, Talevi A, Bellera C, Bruno-Blanch LE, Castro EA. Application of descriptors based on lipinski's rules in the QSPR study of aqueous solubilities. Bioorganic and Medicinal Chemistry. 2007;15(11):3711-9

Puzyn T, Falandysz J. Application and comparison of different chemometric approaches in QSPR modelling of supercooled liquid vapour pressures for chloronaphthalenes. SAR QSAR Environ Res. 2007;18(3-4):299-313

Helguera AM, González MP, D. S. Cordeiro MN, Pérez MÁC. Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds. Toxicol Appl Pharmacol. 2007;221(2):189-202

Toropov AA, Rasulev BF, Leszczynski J. QSAR modeling of acute toxicity for nitrobenzene derivatives towards rats: Comparative analysis by MLRA and optimal descriptors. QSAR and Combinatorial Science. 2007;26(5):686-93

Duchowicz PR, Garro JCM, Andrada MF, Castro EA, Fernández FM. QSPR modeling of heats of combustion for carboxylic acids. QSAR and Combinatorial Science. 2007;26(5):647-52

Gao J, Xu J, Chen B, Zhang Q. A quantitative structure-property relationship study for refractive indices of conjugated polymers. Journal of Molecular Modeling. 2007;13(5):573-8

Varnek A, Kireeva N, Tetko IV, Baskin II, Solov'ev VP. Exhaustive QSPR studies of a large diverse set of ionic liquids: How accurately can we predict melting points? Journal of Chemical Information and Modeling. 2007;47(3):1111-22

Cao C-, Gao S. Bond orbital-connection matrix method to predict refractive indices of alkanes. Chinese Journal of Chemical Physics. 2007;20(2):149,154+i

Gramatica P, Giani E, Papa E. Statistical external validation and consensus modeling: A QSPR case study for koc prediction. J Mol Graph Model. 2007;25(6):755-66

Laszlo T. QSAR studies regarding the inhibition of the carbonic anhydrase by the sulfonamides containing a picolinoyl group. Rev Chim. 2007;58(2):191-4

González-Díaz H, Olazábal E, Santana L, Uriarte E, González-Díaz Y, Castañedo N. QSAR study of anticoccidial activity for diverse chemical compounds: Prediction and experimental assay of trans-2-(2-nitrovinyl)furan. Bioorganic and Medicinal Chemistry. 2007;15(2):962-8

Pasha FA, Srivastava HK, Srivastava A, Singh PP. QSTR study of small organic molecules against tetrahymena pyriformis. QSAR and Combinatorial Science. 2007;26(1):69-84

Tilaoui L, Schilter B, Tran L-, Mazzatorta P, Grigorov M. Integrated computational methods for prediction of the lowest observable adverse effect level of food-borne molecules. QSAR and Combinatorial Science. 2007;26(1):102-8

Singh J, Singh S, Meer S, Agrawal VK, Khadikar PV, Balaban AT. QSPR correlations of half-wave reduction potentials of cata-condensed benzenoid hydrocarbons. Arkivoc. 2006;2006(15):104-19

Costescu A, Moldovan C, Diudea MV. QSAR modeling of steroid hormones. Match. 2006;55(2):315-29

Moorthy NSHN, Trivedi P. QSAR modeling of some 2-methoxy acridones: Cytotoxic in multi drug resistant cells. International Journal of Cancer Research. 2006;2(3):267-76

Duchowicz PR, Castro EA, Fernández FM. Alternative algorithm for the search of an optimal set of descriptors in QSAR-QSPR studies. Match. 2006;55(1):179-92

Tuppurainen K, Korhonen S-, Ruuskanen J. Performance of multicomponent self-organizing regression (MCSOR) in QSAR, QSPR, and multivariate calibration: Comparison with partial least-squares (PLS) and validation with large external data sets. SAR QSAR Environ Res. 2006;17(6):549-61

Diez RP, Duchowicz PR, Castañeta H, Castro EA, Fernández FM, Albesa AG. A theoretical study of a family of new quinoxaline derivatives. J Mol Graph Model. 2006;25(4):487-94

Korichi M, Gerbaud V, Floquet P, Meniai A, Nacef S, Joulia X. Quantitative structure-Odor relationship: Using of multidimensional data analysis and neural network approaches. Computer Aided Chemical Engineering. 2006:21:895-900

Glen RC, Adams SE. Similarity metrics and descriptor spaces - which combinations to choose? QSAR and Combinatorial Science. 2006;25(12):1133-42

Bender A, Jenkins JL, Li Q, Adams SE, Cannon EO, Glen RC. Chapter 9 molecular similarity: Advances in methods, applications and validations in virtual screening and QSAR [Internet]; 2006

Shah JZ, Salim NB. In: A fuzzy kohonen SOM implementation and clustering of bio-active compound structures for drug discovery. Proceedings of the 2006 IEEE symposium on computational intelligence in bioinformatics and computational biology, CIBCB'06; ; 2006. p. 360-5

Fernández M, Carreiras MC, Marco JL, Caballero J. Modeling of acetylcholinesterase inhibition by tacrine analogues using bayesian-regularized genetic neural networks and ensemble averaging. Journal of Enzyme Inhibition and Medicinal Chemistry. 2006;21(6):647-61

Tantishaiyakul V, Worakul N, Wongpoowarak W. Prediction of solubility parameters using partial least square regression. Int J Pharm. 2006;325(1-2):8-14

Liu H, Papa E, Gramatica P. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles. Chem Res Toxicol. 2006;19(11):1540-8

Nigsch F, Bender A, Van Buuren B, Tissen J, Nigsch E, Mitchell JBO. Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization. Journal of Chemical Information and Modeling. 2006;46(6):2412-22

Funar-Timofei S, Fabian WMF, Simu GM, Suzuki T. Quantitative structure-retention relationships (QSRR) for chromatographic separation of disazo and trisazo 4,4'-diaminobenzanilide-based dyes. Croat Chem Acta. 2006;79(2):227-36

Burello E, Rothenberg G. In silico design in homogeneous catalysis using descriptor modelling. International Journal of Molecular Sciences. 2006;7(9):375-404

Luan F, Zhang XY, Zhang HX, Zhang RS, Liu MC, Hu ZD, et al. QSPR study of permeability coefficients through low-density polyethylene based on radial basis function neural networks and the heuristic method. Computational Materials Science. 2006;37(4):454-61

Fernández M, Caballero J. Ensembles of bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines. Chemical Biology and Drug Design. 2006;68(4):201-12

Duchowicz PR, Fernández M, Caballero J, Castro EA, Fernández FM. QSAR for non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorganic and Medicinal Chemistry. 2006;14(17):5876-89

Tabaraki R, Khayamian T, Ensafi AA. Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide. J Mol Graph Model. 2006;25(1):46-54

Ajmani S, Rogers SC, Barley MH, Livingstone DJ. Application of QSPR to mixtures. Journal of Chemical Information and Modeling. 2006;46(5):2043-55

Godavarthy SS, Robinson Jr. RL, Gasem KAM. SVRC-QSPR model for predicting saturated vapor pressures of pure fluids. Fluid Phase Equilib. 2006;246(1-2):39-51

Narasimhan B, Ansari AM, Singh N, Mourya V, Dhake AS. A QSAR approach for the prediction of stability of benzoglycolamide ester prodrugs. Chemical and Pharmaceutical Bulletin. 2006;54(8):1067-71

Hemmateenejad B, Miri R, Safarpour MA, Mehdipour AR. Accurate prediction of the blood-brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling. Journal of Computational Chemistry. 2006;27(11):1125-35

Yap CW, Xue Y, Li ZR, Chen YZ. Application of support vector machines to in silico prediction of cytochrome P450 enzyme substrates and inhibitors. Current Topics in Medicinal Chemistry. 2006;6(15):1593-607

Godavarthy SS, Robinson Jr. RL, Gasem KAM. An improved structure-property model for predicting melting-point temperatures. Industrial and Engineering Chemistry Research. 2006;45(14):5117-26

Crivori P, Poggesi I. Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs. Eur J Med Chem. 2006;41(7):795-808

Gallegos Saliner A. Mini-review on chemical similarity and prediction of toxicity. Current Computer-Aided Drug Design. 2006;2(2):105-22

Banfi S, Caruso E, Buccafurni L, Murano R, Monti E, Gariboldi M, et al. Comparison between 5,10,15,20-tetraaryl- and 5,15-diarylporphyrins as photosensitizers: Synthesis, photodynamic activity, and quantitative structure-activity relationship modeling. J Med Chem. 2006;49(11):3293-304

Caballero J, Garriga M, Fernández M. 2D autocorrelation modeling of the negative inotropic activity of calcium entry blockers using bayesian-regularized genetic neural networks. Bioorganic and Medicinal Chemistry. 2006;14(10):3330-40

Duchowicz PR, Castañeta H, Castro EA, Fernández FM, Vicente JL. QSPR prediction of the dubinin-radushkevich's k parameter for the adsorption of organic vapors on BPL carbon. Atmos Environ. 2006;40(16):2929-34

González MP, Caballero J, Helguera AM, Garriga M, González G, Fernández M. 2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors. Bull Math Biol. 2006;68(4):735-51

Yan D, Jiang X, Yu G, Zhao Z, Bian Y, Wang F. Quantitative structure-toxicity relationships of organophosphorous pesticides to fish (cyprinus carpio). Chemosphere. 2006;63(5):744-50

Morales AH, Duchowicz PR, Pérez MAC, Castro EA, Cordeiro MNDS, González MP. Application of the replacement method as a novel variable selection strategy in QSAR. 1. carcinogenic potential. Chemometrics Intellig Lab Syst. 2006;81(2):180-7

Liu F, Liang Y, Cao C. QSPR modeling of thermal conductivity detection response factors for diverse organic compound. Chemometrics Intellig Lab Syst. 2006;81(2):120-6

Pavan M, Netzeva TI, Worth AP. Validation of a QSAR model for acute toxicity. SAR QSAR Environ Res. 2006;17(2):147-71

Yap CW, Xue Y, Li H, Li ZR, Ung CY, Han LY, et al. Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. Mini-Reviews in Medicinal Chemistry. 2006;6(4):449-59

Gramatica P. WHIM descriptors of shape. QSAR and Combinatorial Science. 2006;25(4):327-32

Xu J, Zheng Z, Chen B, Zhang Q. A linear QSPR model for prediction of maximum absorption wavelength of second-order NLO chromophores. QSAR and Combinatorial Science. 2006;25(4):372-9

Piclin N, Pintore M, Wechman C, Roncaglioni A, Benfenati E, Chretien JR. Ecotoxicity prediction by adaptive fuzzy partitioning: Comparing descriptors computed on 2D and 3D structures. SAR QSAR Environ Res. 2006;17(2):225-51

Fossa P, Mosti L, Bondavalli F, Schenone S, Ranise A, Casolino C, et al. Affinity prediction on A1 adenosine receptor agonists: The chemometric approach. Bioorganic and Medicinal Chemistry. 2006;14(5):1348-63

Morales AH, Pérez MAC, Combes RD, González MP. Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity. Toxicology. 2006;220(1):51-62

Vilar S, Santana L, Uriarte E. Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action. J Med Chem. 2006;49(3):1118-24

Mwense M, Wang XZ, Buontempo FV, Horan N, Young A, Osborn D. QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions. SAR QSAR Environ Res. 2006;17(1):53-73

Maldonado AG, Doucet JP, Petitjean M, Fan B-. Molecular similarity and diversity in chemoinformatics: From theory to applications. Mol Divers. 2006;10(1):39-79

Prabhakar YS, Gupta MK, Roy N, Venkateswarlu Y. A high dimensional QSAR study on the aldose reductase inhibitory activity of some flavones: Topological descriptors in modeling the activity. Journal of Chemical Information and Modeling. 2006;46(1):86-92

Gupta MK, Prabhakar YS. Topological descriptors in modeling the antimalarial activity of 4-(3',5'-disubstituted anilino)quinolines. Journal of Chemical Information and Modeling. 2006;46(1):93-102

Xiao Y-, Harris R, Bayram E, Peter Santago II, Schmitt JD. Supervised self-organizing maps in drug discovery. 2. improvements in descriptor selection and model validation. Journal of Chemical Information and Modeling. 2006;46(1):137-44

Habibi-Yangjeh A, Danandeh-Jenagharad M, Nooshyar M. Prediction acidity constant of various benzoic acids and phenols in water using linear and nonlinear QSPR models. Bulletin of the Korean Chemical Society. 2005;26(12):2007-16

Xu J, Guo B, Chen B, Zhang Q. A QSPR treatment for the thermal stabilities of second-order NLO chromophore molecules. Journal of Molecular Modeling. 2005;12(1):65-75

Li H, Yap CW, Xue Y, Li ZR, Ung CY, Han LY, et al. Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents. Drug Dev Res. 2005;66(4):245-59

Randic M, Pompe M. Retro-regression - A way to resolve multivariate regression ambiguities. Acta Chimica Slovenica. 2005;52(4):408-16

Pavan M, Consonni V, Todeschini R. Partial ranking models by genetic algorithm variable subset selection (GAVSS) approach for environmental priority settings. Match. 2005;54(3):583-609

Montero-Torres A, Vega MC, Marrero-Ponce Y, Rolón M, Gómez-Barrio A, Escario JA, et al. A novel non-stochastic quadratic fingerprints-based approach for the 'in silico' discovery of new antitrypanosomal compounds. Bioorganic and Medicinal Chemistry. 2005;13(22):6264-75

Cartmell J, Enoch S, Krstajic D, Leahy DE. Automated QSPR through competitive workflow. J Comput Aided Mol Des. 2005;19(11):821-33

González MP, Terán C, Teijeira M, González-Moa MJ. GETAWAY descriptors to predicting A2A adenosine receptors agonists. Eur J Med Chem. 2005;40(11):1080-6

Safa F, Hadjmohammadi MR. Use of topological indices of organic sulfur compounds in quantitative structure-retention relationship study. QSAR and Combinatorial Science. 2005;24(9):1026-32

Baumann K. Chance correlation in variable subset regression: Influence of the objective function, the selection mechanism, and ensemble averaging. QSAR and Combinatorial Science. 2005;24(9):1033-46

Fernández M, Tundidor-Camba A, Caballero J. Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d] pyrimidine derivatives using artificial neural network ensembles. Journal of Chemical Information and Modeling. 2005;45(6):1884-95

Gramatica P, Papa E. An update of the BCF QSAR model based on theoretical molecular descriptors. QSAR and Combinatorial Science. 2005;24(8):953-60

Caetano S, Decaestecker T, Put R, Daszykowski M, Van Bocxlaer J, Vander Heyden Y. Exploring and modelling the responses of electrospray and atmospheric pressure chemical ionization techniques based on molecular descriptors. Anal Chim Acta. 2005;550(1-2):92-106

Duchowicz PR, Castro EA, Fernández FM, Gonzalez MP. A new search algorithm for QSPR/QSAR theories: Normal boiling points of some organic molecules. Chemical Physics Letters. 2005;412(4-6):376-80

Papa E, Villa F, Gramatica P. Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in pimephales promelas (fathead minnow). Journal of Chemical Information and Modeling. 2005;45(5):1256-66

Weber L. Current status of virtual combinatorial library design. QSAR and Combinatorial Science. 2005;24(7):809-23

Puzyn T, Falandysz J. Octanol/water partition coefficients of chloronaphthalenes. Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering. 2005;40(9):1651-63

Prabhakar YS, Rawal RK, Gupta MK, Solomon VR, Katti SB. Topological descriptors in modeling the HIV inhibitory activity of 2-aryl-3-pyridyl-thiazolidin-4-ones. Combinatorial Chemistry and High Throughput Screening. 2005;8(5):431-7

González-Díaz H, Pérez-Bello A, Uriarte E. Stochastic molecular descriptors for polymers. 3. markov electrostatic moments as polymer 2D-folding descriptors: RNA-QSAR for mycobacterial promoters. Polymer. 2005;46(17):6461-73

Fernández M, Tundidor-Cambah A, Caballero JM. 2D autocorrelation modeling of the activity of trihalobenzocycloheptapyridine analogues as farnesyl protein transferase inhibitors. Molecular Simulation. 2005;31(8):575-84

Buontempo FV, Wang XZ, Mwense M, Horan N, Young A, Osborn D. Genetic programming for the induction of decision trees to model ecotoxicity data. Journal of Chemical Information and Modeling. 2005;45(4):904-12

Smith JR, Kholodovych V, Knight D, Kohn J, Welsh WJ. Predicting fibrinogen adsorption to polymeric surfaces in silico: A combined method approach. Polymer. 2005;46(12):4296-306

Hancock T, Put R, Coomans D, Vander Heyden Y, Everingham Y. A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemometrics Intellig Lab Syst. 2005;76(2):185-96

González-Díaz H, Cruz-Monteagudo M, Viña D, Santana L, Uriarte E, De Clercq E. QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices. Bioorganic and Medicinal Chemistry Letters. 2005;15(6):1651-7

Turabekova MA, Rasulev BF. QSAR analysis of the structure-toxicity relationship of aconitum and delphinium diterpene alkaloids. Chemistry of Natural Compounds. 2005;41(2):213-9

Gramatica P, Pilutti P, Papa E. Ranking of phenols for abiotic oxidation in aqueous environment: A QSPR approach. Ann Chim. 2005;95(3-4):199-209

Kabankin AS, Gabrielyan LI. Relationship between structure and hepatoprotector activity of adamantane derivatives. part 2. application of autocorrelative, substructural, and 3D molecular descriptors. Pharmaceutical Chemistry Journal. 2005;39(3):135-9

Puzyn T, Falandysz J. Computational estimation of logarithm of n-octanol/air partition coefficient and subcooled vapor pressures of 75 chloronaphthalene congeners. Atmos Environ. 2005;39(8):1439-46

Papa E, Battaini F, Gramatica P. Ranking of aquatic toxicity of esters modelled by QSAR. Chemosphere. 2005;58(5):559-70

Carrera G, Aires-de-Sousa J. Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks. Green Chem. 2005;7(1):20-7

Hemmateenejad B, Safarpour MA, Miri R, Nesari N. Toward an optimal procedure for PC-ANN model building: Prediction of the carcinogenic activity of a large set of drugs. Journal of Chemical Information and Modeling. 2005;45(1):190-9

Yap CW, Chen YZ. Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. J Pharm Sci. 2005;94(1):153-68

Marrero Ponce Y. Total and local (atom and atom type) molecular quadratic indices: Significance interpretation, comparison to other molecular descriptors, and QSPR/QSAR applications. Bioorganic and Medicinal Chemistry. 2004;12(24):6351-69

Asikainen AH, Ruuskanen J, Tuppurainen KA. Consensus kNN QSAR: A versatile method for predicting the estrogenic activity of organic compounds in silico. A comparative study with five estrogen receptors and a large, diverse set of ligands. Environmental Science and Technology. 2004;38(24):6724-9

Gramatica P, Battaini F, Papa E. QSAR prediction of physicochemical properties of esters. Fresenius Environ Bull. 2004;13(11 B):1258-62

Ciubotariu D, Medeleanu M, Vlaia V, Olariu T, Ciubotariu C, Dragos D, et al. Molecular van der waals space and topological indices from the distance matrix. Molecules. 2004;9(12):1053-78

Milicevic A, Nikolic S, Trinajstic N. On reformulated zagreb indices. Mol Divers. 2004;8(4):393-9

Estrada E, Delgado EJ, Alderete JB, Jaña GA. Quantum-connectivity descriptors in modeling solubility of environmentally important organic compounds. Journal of Computational Chemistry. 2004;25(14):1787-96

Núñez MB, Maguna FP, Okulik NB, Castro EA. QSAR modeling of the MAO inhibitory activity of xanthones derivatives. Bioorganic and Medicinal Chemistry Letters. 2004;14(22):5611-7

Marrero-Ponce Y. Linear indices of the "molecular pseudograph's atom adjacency matrix": Definition, significance-interpretation, and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors. J Chem Inf Comput Sci. 2004;44(6):2010-26

Gramatica P, Pilutti P, Papa E. A tool for the assessment of VOC degradability by tropospheric oxidants starting from chemical structure. Atmos Environ. 2004;38(36):6167-75

Schefzick S, Kibbey C, Bradley MP. Prediction of HPLC conditions using QSPR techniques: An effective tool to improve combinatorial library design. J Comb Chem. 2004;6(6):916-27

Hemmateenejad B. Optimal QSAR analysis of the carcinogenic activity of drugs by correlation ranking and genetic algorithm-based PCR. J Chemometrics. 2004;18(11):475-85

Kholodovych V, Smith JR, Knight D, Abramson S, Kohn J, Welsh WJ. Accurate predictions of cellular response using QSPR: A feasibility test of rational design of polymeric biomaterials. Polymer. 2004;45(22):7367-79

Pavan M, Mauri A, Todeschini R. Total ranking models by the genetic algorithm variable subset selection (GA-VSS) approach for environmental priority settings. Analytical and Bioanalytical Chemistry. 2004;380(3 SPEC.ISS.):430-44

Winkler DA. Neural networks in ADME and toxicity prediction. Drugs of the Future. 2004;29(10):1043-57

Ramos De Armas R, González Dí-az H, Molina R, Pérez González M, Uriarte E. Stochastic-based descriptors studying peptides biological properties: Modeling the bitter tasting threshold of dipeptides. Bioorganic and Medicinal Chemistry. 2004;12(18):4815-22

Guha R, Serra JR, Jurs PC. Generation of QSAR sets with a self-organizing map. J Mol Graph Model. 2004;23(1):1-14

Mwense M, Wang XZ, Buontempo FV, Horan N, Young A, Osborn D. Prediction of noninteractive mixture toxicity of organic compounds based on a fuzzy set method. J Chem Inf Comput Sci. 2004;44(5):1763-73

Gramatica P, Pilutti P, Papa E. Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling. J Chem Inf Comput Sci. 2004;44(5):1794-802

Farkas O, Héberger K, Zenkevich IG. Quantitative structure-retention relationships XIV: Prediction of gas chromatographic retention indices for saturated O-, N-, and S-heterocyclic compounds. Chemometrics Intellig Lab Syst. 2004;72(2):173-84

Pérez González M, Terán Moldes MDC. A TOPS-MODE approach to predict affinity for A1 adenosine receptors. 2-(arylamino)adenosine analogues. Bioorganic and Medicinal Chemistry. 2004;12(11):2985-93

Stanton DT, Mattioni BE, Knittel JJ, Jurs PC. Development and use of hydrophobic surface area (HSA) descriptors for computer-assisted quantitative structure-activity and structure-property relationship studies. J Chem Inf Comput Sci. 2004;44(3):1010-23

Huang J, Yu G, Yang X, Zhang Z-. Predicting physico-chemical properties of polychlorinated diphenyl ethers (PCDEs): Potential persistent organic pollutants (POPs). Journal of Environmental Sciences. 2004;16(2):204-7

Garkani-Nejad Z, Karlovits M, Demuth W, Stimpfl T, Vycudilik W, Jalali-Heravi M, et al. Prediction of gas chromatographic retention indices of a diverse set of toxicologically relevant compounds. Journal of Chromatography A. 2004;1028(2):287-95

Kovatcheva A, Golbraikh A, Oloff S, Xiao Y-, Zheng W, Wolschann P, et al. Combinatorial QSAR of ambergris fragrance compounds. J Chem Inf Comput Sci. 2004;44(2):582-95

González MP, Helguera AM, Díaz HG. A TOPS-MODE approach to predict permeability coefficients. Polymer. 2004;45(6):2073-9

Asikainen AH, Ruuskanen J, Tuppurainen KA. Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds. SAR QSAR Environ Res. 2004;15(1):19-32

Godavarthy SS, Ravindranath D, Robinson Jr. RL, Gasem KAM. In: Generalized SVRC-QSPR model for prediction of saturated vapor pressures and phase densities. 2004 AIChE spring meeting, conference proceedings; ; 2004

Gramatica P, Papa E, Battaini F. Ranking and classification of non-ionic organic pesticides for environmental distribution: A QSAR approach. Int J Environ Anal Chem. 2004;84(1-3):65-74

Szántai-Kis C, Kövesdi I, Kéri G, Orfi L. Validation subset selections for extrapolation oriented QSPAR models. Mol Divers. 2003;7(1):37-43

Young SS, Wang M, Gu F. Design of diverse and focused combinatorial libraries using an alternating algorithm. J Chem Inf Comput Sci. 2003;43(6):1916-21

González MP, Helguera AM. TOPS-MODE versus DRAGON descriptors to predict permeability coefficients through low-density polyethylene. J Comput Aided Mol Des. 2003;17(10):665-72

Gramatica P, Consonni V, Pavan M. Prediction of aromatic amines mutagenicity from theoretical molecular descriptors. SAR QSAR Environ Res. 2003;14(4):237-50

Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect. 2003;111(10):1361-75

Gramatica P, Pilutti P, Papa E. Predicting the NO3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors. Atmos Environ. 2003;37(22):3115-24

Lapinsh M, Prusis P, Mutule I, Mutulis F, Wikberg JES. QSAR and proteo-chemometric analysis of the interaction of a series of organic compounds with melanocortin receptor subtypes. J Med Chem. 2003;46(13):2572-9

Nikolic S, Kovacevic G, Milicevic A, Trinajstic N. The zagreb indices 30 years after. Croat Chem Acta. 2003;76(2):113-24

Vukicevic D, Trinajstic N. Modified zagreb M2 index - comparison with the randic connectivity index for benzenoid systems. Croat Chem Acta. 2003;76(2):183-7

Gramatica P, Papa E. QSAR modeling of bioconcentration factor by theoretical molecular descriptors. QSAR and Combinatorial Science. 2003;22(3):374-85

Stiefl N, Baumann K. Mapping property distributions of molecular surfaces: Algorithm and evaluation of a novel 3D quantitative structure - activity relationship technique. J Med Chem. 2003;46(8):1390-407

Yao X, Fan B, Doucet JP, Panaye A, Liu M, Zhang R, et al. Quantitative structure property relationship models for the prediction of liquid heat capacity. QSAR and Combinatorial Science. 2003;22(1):29-48

Hollas B. An analysis of the autocorrelation descriptor for molecules. Journal of Mathematical Chemistry. 2003;33(2):91-101

Hajduk PJ, Mendoza R, Petros AM, Huth JR, Bures M, Fesik SW, et al. Ligand binding to domain-3 of human serum albumin: A chemometric analysis. J Comput Aided Mol Des. 2003;17(2-4):93-102

Andersson PL, Maran U, Fara D, Karelson M, Hermens JLM. General and class specific models for prediction of soil sorption using various physicochemical descriptors. J Chem Inf Comput Sci. 2002;42(6):1450-9

Mosier PD, Jurs PC. QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. J Chem Inf Comput Sci. 2002;42(6):1460-70

Yao X, Liu M, Zhang X, Hu Z, Fan B. Radial basis function network-based quantitative structure-property relationship for the prediction of henry's law constant. Anal Chim Acta. 2002;462(1):101-17

Yao X, Wang Y, Zhang X, Zhang R, Liu M, Hu Z, et al. Radial basis function neural network-based QSPR for the prediction of critical temperature. Chemometrics Intellig Lab Syst. 2002;62(2):217-25

Consonni V, Todeschini R, Pavan M, Gramatica P. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. application of the novel 3D molecular descriptors to QSAR/QSPR studies. J Chem Inf Comput Sci. 2002;42(3):693-705

El-Taher S, El-Sawy KM, Hilal R. Electronic structure of some adenosine receptor antagonists. VQSAR investigation. J Chem Inf Comput Sci. 2002;42(2):386-92

Gramatica P, Consolaro F, Pozzi S. QSAR approach to POPs screening for atmospheric persistence. Chemosphere. 2001;43(4-7):655-64

Randic M, Vracko M, Novic M, Basak SC. On ordering of folded structures. Match. 2000;42:181-231