Use computation to decide what deserves synthesis and assay.
Coordinate targets, molecular candidates, QSAR, docking, developability screening, synthesis planning and measured assay evidence in a single iterative discovery programme.
Start with the bottleneck that consumes expensive evidence.
Drug discovery is an information-allocation problem as much as a search problem. Computational tools can evaluate far more candidates than a laboratory can synthesize or assay, but predictions only become useful when they are tied to target endpoints, experimental evidence and a disciplined progression process.
Reduce a large molecular search space to a smaller set of experimentally meaningful decisions, while preserving the distinction between generated structures, computational predictions and measured assay outcomes.
Connect candidate evidence to the human-system context it is intended to inform.
The HumanTwin model gives the programme a biological visual layer while LargeQuant preserves the distinction between generated candidates, computational predictions and measured evidence.
Open HumanTwin →From objective to evidence and next action.
- 01Define the biological target, assay endpoints and quantitative progression criteria.
- 02Register known compounds, molecular structures, measured endpoints and campaign stages.
- 03Fit quantitative property/QSAR models where the available evidence meets the model requirements.
- 04Run trusted docking workflows when prepared receptor/ligand artifacts and runtime dependencies are available.
- 05Evaluate developability descriptors and multi-objective constraints alongside potency-oriented predictions.
- 06Generate bounded molecular proposals using approved chemistry tooling and retain them as proposals until enrolled.
- 07Create synthesis and experiment plans for selected compounds and connect execution/result ingestion where available.
- 08Ingest measured assay results, update the evidence state and re-rank the next set of compounds.
The programme combines modelling, execution and evidence.
Campaign records
Targets, compounds, stages, assays and candidate evaluations.
QSAR
Native quantitative property modelling for supported numerical endpoints.
Docking
Trusted AutoDock Vina execution boundary where runtime and prepared artifacts are available.
Developability
Descriptor-based screens for recorded chemical property constraints.
Molecular proposal
Bounded analogue/proposal generation through approved chemistry adapters.
Experiment loop
Synthesis planning, result ingestion, evidence capture and iterative re-ranking.
What connects into the programme.
- Target and assay definitions
- Molecular structures and measured endpoints
- Prepared docking artifacts where used
- Chemical/property constraints
- Synthesis feasibility or retrosynthesis outputs
- Laboratory assay results
What the team gets back.
- Ranked compound evaluations
- QSAR predictions with model evidence
- Docking execution records and extracted scores
- Developability assessments
- Molecular proposals and synthesis/experiment plans
- Measured assay observations feeding the next campaign round
Why the operating loop matters.
- Use computation to narrow the set of compounds that consume synthesis and assay capacity.
- Keep measured evidence in control of endpoint truth when it exists.
- Combine potency-oriented modelling with developability and other constraints.
- Preserve the progression history of every candidate and every experimental result.
- Create a repeatable loop between computational design and laboratory evidence.