The autonomous discovery funnel, on top of a 122M-compound engine
Traditional pipelines break across tools, teams, and weeks of waiting. The engine collapses the entire process into a single workflow — twelve stages from target identification to patient stratification, run from one conversation.
Target Discovery
108K associations
Target Validation
5 evidence streams
Literature & Landscape
14K papers · 2.4K patents
Known Actives
2.4M ChEMBL · IC50/Ki
ADMET Profiling
122M profiled · <50ms
Compliance
8 jurisdictions · inline
Lead Optimization
30+ scaffolds
Molecular Docking
AutoDock-GPU · PLIP
Clinical Outcomes
AUROC 0.756
MD Simulation
GROMACS · MM-GBSA
FEP
Alchemical ΔΔG · optional
Patient Stratification
56 pharmacogenes
Target Discovery
108K associations
Target Validation
5 evidence streams
Literature & Landscape
14K papers · 2.4K patents
Known Actives
2.4M ChEMBL · IC50/Ki
ADMET Profiling
122M profiled · <50ms
Compliance
8 jurisdictions · inline
Lead Optimization
30+ scaffolds
Molecular Docking
AutoDock-GPU · PLIP
Clinical Outcomes
AUROC 0.756
MD Simulation
GROMACS · MM-GBSA
FEP
Alchemical ΔΔG · optional
Patient Stratification
56 pharmacogenes
The discovery funnel
Each stage has its own page with the science problem, proof, and a one-sentence prompt.
Target Discovery
Identify viable drug targets from 108,000 genomic associations - before committing lab resources.
Target Validation
Adversarially stress-test the target across five evidence streams - omics, trials, literature, ChEMBL, and competitive landscape - before spending credits.
Literature & Landscape
Search the full research landscape in one query - 14,000 papers and 2,400 patents.
Known Actives
Pull measured IC50/Ki potency from 2.4M ChEMBL bioactive compounds to seed optimization.
ADMET Profiling
Eliminate unsafe candidates early. 122M compounds pre-profiled with 31 ML models.
Compliance
Avoid regulatory dead-ends - 8 jurisdictions screened inline at every stage.
Lead Optimization
Generate optimized candidates automatically. Every variant enriched with ADMET and compliance.
Molecular Docking
Validate binding before you run experiments. GPU-accelerated with strain energy validation.
Clinical Outcomes
Predict Phase I clearance probability before committing to IND-enabling studies.
MD Simulation
Confirm stability with production-scale dynamics. GROMACS GPU with MM-GBSA.
FEP
Optional alchemical free-energy validation - relative binding ΔΔG via GROMACS for the candidates that merit it.
Patient Stratification
Identify which patient populations will respond - 56 pharmacogenes, 135K resistance variants.
Start a discovery pipeline
Twelve stages. One conversation. Sign up and run your first funnel.