As being part of the fields of computational chemistry and machine learning, QSAR and QSPR (Quantitative Structure–Activity/Property Relationship) modeling take advantage of computational techniques to derive meaningful insights from chemical and molecular data. These techniques encompass a range of methods designed to analyze, model, and predict various biological and chemical-physical activities.
The ALChemy suite is designed to face every step of the modeling process, from the features selection to the predictive model deployment.
The predictive models obtained serve as invaluable tools for identifying potential candidates with desirable properties in the plethora of chemical compounds, thus saving time, resources, and effort in experimental work.
As the fields of cheminformatics and computational chemistry continue to evolve, QSAR/QSPR modeling remains a cornerstone of innovation, enabling scientists and researchers to harness the power of data-driven insights to revolutionize drug discovery and material science.
Thanks to the suite’s capacity to handle the full range of complex and time consuming tasks involved in the QSAR modelling process, ALChemy represents a powerful support for industries and research institutions in the invaluable job of identifying potential candidates with desirable properties.
Currently, there are two products included in the suite that can be executed either separately or sequentially one after the other:
ALChemy accepts as input datasets of molecular descriptors calculated by one of the many solutions available in the market by now (Dragon7, alvaDesc, any free software) with the possibility to make any kind of customization to integrate it in your calculation pipeline.