Context
A cosmetic company has been using an effective preservative for years, but it is increasingly under scrutiny from regulatory authorities and consumers, who are questioning its skin safety profile. The R&D team needs a substitute that guarantees the same antimicrobial efficacy, but with a more solid toxicological profile — and ideally also better compatibility with a “clean” product positioning. Where to start, among hundreds of potentially analogous molecules?
THE CHALLENGE
In the early stages of research and development, a promising molecule — whether an active ingredient under study or an ingredient to be replaced — can raise many questions: which biological targets might it engage? Is its profile consistent with a given therapeutic area? Which evidence is solid, and which is merely exploratory hypothesis? And above all: which experiments should be run first?
At this stage, testing too many candidates can be costly and unfocused; testing too few, without a rationale, can lead to discarding the best alternative or, worse, choosing one that is poorly supported. Our analysis helps build an experimental priority based on available evidence, biological consistency and signal quality, starting from the structure of the original preservative to map the entire neighbourhood of structurally similar alternatives and evaluate their profile with the same rigor.
Our Similarity-Based Target Intelligence pipeline was built to answer these questions, turning a chemical structure into an interpretable, traceable, decision-oriented biological map.
THE APPROACH
The service starts from a molecule of interest — identified via name, SMILES, InChIKey, PubChem CID, ChEMBL ID or other identifiers — and reconstructs its chemical-pharmacological neighbourhood. In other words, it identifies structurally similar compounds, analyzes their known biological targets, and gathers available experimental activity, mechanistic annotations and information from selected public databases.
The result is not a simple list of targets, but a reasoned assessment of the strength of the evidence — that is, how solidly the available literature documents the interaction between a safer candidate and the biological targets relevant to antimicrobial efficacy and skin safety.
The pipeline distinguishes between direct support on the molecule, signals derived from similar compounds, mechanistic evidence, experimental activity, external hypotheses from similarity, pathway-adjacent signals, weak or inactive evidence, and absence of support. This approach helps avoid overinterpretation and formulate scientifically sounder claims — a non-negotiable requirement when the claim in question concerns the safety of an ingredient intended for skin contact.
THE WORKFLOW
The workflow included the retrieval of similar compounds, local recalculation of similarity using multiple metrics, aggregation of compound-target associations, identification of the most recurring targets, evaluation of the best available activities and, where required, verification of a client-defined receptor or target panel.
For each target identified, the service provided the company’s R&D team with a classified reading: priority target, secondary signal, exploratory hypothesis, indirect support, weak signal, or unsupported target. This allows R&D teams to move from a fragmented set of data to a clear roadmap for next steps.
In this case, the deliverables included an executive report, technical tables, a full Excel workbook, charts, compound-target maps, evidence summaries, recommendations for experimental follow-up, and materials useful for developing the new product containing the replaced preservative.
THE BENEFITS
The value of the service is twofold. On one hand, it accelerates the biological understanding of the molecule. On the other, it reduces the risk of investing in uninformative experiments, helping to select targets and assays with greater rationale.
This solution is particularly useful for:
- assessing the biological potential of new small molecules;
- supporting drug repurposing activities;
- identifying safer or more sustainable substitutes for cosmetic ingredients under regulatory or reputational scrutiny;
- designing more targeted experimental panels;
- comparing analogues or candidates within a chemical series;
- building scientific dossiers for partners, investors or internal stakeholders;
- identifying supportable claims and distinguishing hypotheses still to be validated.
Our philosophy is simple: not to replace experimental validation, but to make it smarter.
Through a combined approach of chemoinformatics, bioactivity mining, target intelligence and conservative interpretation of evidence, we help companies — from pharma to cosmetics — turn a molecule into a clearer, more defensible, action-oriented development strategy.