A quantitative operating layer for computational discovery.
LargeQuant brings molecular candidates, numerical property models, docking, developability, synthesis planning, experimental evidence and reproducibility into one discovery programme.
Computational scale only matters if it improves experimental choice.
The laboratory is the scarce resource. Discovery teams can generate or screen far more molecular ideas than they can synthesize and assay, so the platform must help decide which candidate deserves the next experiment and then learn from the measured result. LargeQuant is designed around that closed loop.
Put biological model state, evidence and uncertainty in the same visual context.
HumanTwin provides the interactive anatomical layer. LargeQuant provides the quantitative evidence envelope underneath it, so a region can be inspected together with model state, evidence count, uncertainty and provenance.
Explore HumanTwin →High-value quantitative workflows.
Hit and lead prioritisation
Combine quantitative endpoints, docking evidence, measured assays and multi-objective constraints to rank compounds.
QSAR / property modelling
Fit quantitative property models where endpoint evidence is sufficient and retain model/evaluation provenance.
Docking workflows
Execute prepared docking cases through trusted adapters and preserve extracted scores and artifacts.
Developability screening
Evaluate recorded descriptor constraints alongside target-oriented predictions.
Synthesis & experiment planning
Turn selected compounds into structured synthesis and assay plans linked to the campaign record.
Measured-result feedback
Ingest assay outcomes as measured evidence and use them to update candidate progression and the next research round.
Connect the domain model to the evidence and decision layer.
Quantitative infrastructure around your domain expertise.
Drug campaign system
Targets, compounds, assays, stages and candidate evaluations.
QSAR
Numerical molecular property modelling for supported endpoints.
Docking adapters
Trusted execution around prepared docking artifacts.
Developability
Descriptor-based quantitative screening.
Molecular proposals
Bounded chemistry-tool integrations for candidate generation.
Evidence loop
Measured assay results flow back into the programme without being conflated with predictions.
Designed to connect to the stack you already operate.
- Molecular structure collections
- Assay / screening databases
- Cheminformatics toolchains
- Docking runtimes and prepared receptor/ligand artifacts
- Retrosynthesis/synthesis planning tools
- Laboratory result feeds
Built for cross-functional quantitative work.
- Computational Chemistry
- Medicinal Chemistry
- Drug Discovery Informatics
- Translational / Discovery Biology
- Platform R&D
- Scientific Data & AI Teams
Keep the basis of the decision inspectable.
Computed affinity, QSAR output, descriptor screens and measured assay values remain separate evidence classes. That separation is essential for using computation to guide experiments without presenting prediction as measurement.
Explore evidence & provenance →