Forster Discovery uses structure-based modelling, advanced physics-based molecular analysis, docking and cheminformatics to help prioritise compounds, test hypotheses and focus experimental work where it matters most.
Better prioritisation. Fewer low-value compounds. Clearer experimental hypotheses.
The toolbox is used when a chemistry or structural decision would benefit from systematic computational analysis before more compounds are made or more experiments are commissioned.
Prioritise plausible designs when synthesis capacity or assay bandwidth is limited.
Compare proposed binding modes, interactions and alternative structural interpretations.
Use structural and series-level analysis to generate testable explanations for unexpected activity patterns.
Compare stereochemistry, conformations, protonation or alternative poses where molecular state may influence the decision.
Reduce larger enumerated sets to a chemically and experimentally practical shortlist.
Stress-test the current design rationale against alternative poses, analogues and structural comparisons.
Protein-structure selection, preparation, alignment, binding-site comparison and interaction analysis to establish a useful structural basis for the question.
Pose generation, scoring, comparison and interaction-based review, with emphasis on comparative interpretation rather than a single docking score.
Integrating structural compatibility, medicinal-chemistry context, molecular properties and project-specific criteria to rank ideas for follow-up.
Similarity, substructure, matched-pair, scaffold and analogue analysis using reproducible RDKit / Python workflows.
Filtering and triage of virtual or enumerated compound sets, including pharmacophore and structure-guided design support where appropriate.
Purpose-built analyses for a defined program question, rather than forcing every project through the same fixed software pipeline.
Many plausible analogues, limited synthesis capacity and a need to focus the next design cycle.
Activity changes are clear, but the structural explanation is not. Computational comparison can help generate testable hypotheses.
Independent review of CRO compound proposals, design logic or virtual libraries before resources are committed.
Programs where multiple ligand, protein and interface geometries may need to be considered rather than a simple binary binding model.
Hundreds or thousands of proposed compounds need rational triage before synthesis or procurement.
A program already has a structural hypothesis, but an independent computational assessment would reduce decision risk.
Docking scores, structural models and computational rankings are treated as comparative evidence rather than direct predictions of potency, degradation or clinical performance. Conclusions are reported with uncertainty and interpreted alongside experimental data, medicinal chemistry and synthetic feasibility.
Share the question and whatever data or structures you already have. We can then define whether computational analysis is likely to change the decision and what a useful, bounded piece of work would look like.