Drug Discovery

From a validated target to a lead candidate. The full Design-Build-Test-Learn loop runs through the LabOS agent mesh — Hypothesis Agent designs the campaign, Execution Broker routes nodes to partner CROs, QC Agent enforces quality gates, Learning Agent updates priors after every round.

Where we intervene

StageWhat LabOS doesCycle compression vs. traditional
Hit identificationVirtual screen (Design Agent over chem libraries) + focused HTS routed to Syngene / Aragen2–3 months vs. 6–12; 80% lower spend
Hit-to-leadAI-designed SAR cycles, agent picks next compounds, wet-lab via partner CRO4–6 months vs. 12–18
Lead optimizationADMET prediction + iterative wet-lab confirmation, closed-loop50% faster, 50% cheaper
Candidate selectionFinal lead nomination with structured PK / PD profile, tox flags, IP analysis2–4 weeks vs. months

Why agentic DBTL works here

The bottleneck in drug discovery has always been the cycle time between “we synthesized these molecules” and “now what.” The Learning Agent eliminates that gap: it ingests every round’s wet-lab results, updates the SAR model, and the Design Agent proposes the next batch within hours, not weeks. Across a typical hit-to-lead campaign, this compresses 18 months into 4–6.

Engagement

$1M–$3M per discovery program. Milestone-gated: hit identification → hit confirmation → lead nomination. Customers can exit at any milestone. Full sprint from validated target to IND-enabling lead: 12–18 months total.

SAR cycle compression

Traditional SAR: design batch → 6-week CRO queue → results → team meeting → repeat. LabOS loop: Learning Agent ingests results overnight; Design Agent proposes next batch by morning; Execution Broker slots CRO capacity within 48 hours. Typical compression: 18 months → 4–6 months for hit-to-lead.

When cycles fail

QC Agent flags anomalous IC50 shifts or plate effects. Learning Agent down-weights contaminated rounds rather than propagating bad SAR. Scientist sign-off required before next batch commit — see agentic risk controls.