Our autonomous agents screen existing drugs for new indications and design novel compounds from scratch — delivering evidence-backed, end-to-end reports in days, not years.
▶ Watch the agent generate a full drug-repurposing report — start to finish in under 2 minutes.
Our AI agents analyze vast databases of existing drugs to identify new therapeutic applications, significantly reducing development time and costs while expanding treatment options.
Accelerate the discovery of novel compounds with our machine learning algorithms that predict efficacy, toxicity, and pharmacokinetic properties with remarkable accuracy.
Seamlessly integrate clinical, genomic, and chemical data sources to uncover hidden patterns and relationships that human researchers might miss.
You don't need to learn our tooling. You give us a target; our agents do the science and hand back something your team can act on. Every step is transparent and traceable back to its source data.
Tell us the disease or indication — and, for repurposing, any compounds you want assessed. That's the only input we need to start.
30+ specialized agents assemble disease biology, drug and pharmacogenomic data, molecular docking, ADMET, and clinical-trial evidence — in parallel.
Candidates are scored and simulated, then the system critiques its own findings — surfacing data gaps and a calibrated confidence level, not just a number.
A ranked, evidence-backed report with a clear GO / CONDITIONAL-GO recommendation, risks, mitigations, and concrete next steps for the lab.
See it for real → repurposing report · discovery report
These are real, end-to-end analyses produced autonomously by our agents — including scoring, molecular docking, ADMET, clinical evidence, and the system's own self-critique. Read them in full; nothing is edited out.
Evidence-backed repurposing hypothesis with strong TNF docking (−12.1 kcal/mol), calibrated confidence, and explicit risks & mitigations.
View Full ReportFull discovery pipeline — target ID, generative chemistry, virtual screening, ADMET, clinical design, and regulatory strategy — in one autonomous run.
View Full ReportWhether you evaluate the science or sign off on the spend, the platform is designed to give you exactly what you need to say yes with confidence.
Computational biologists, medicinal chemists, translational researchers
Biotech founders, BD & pipeline owners, R&D executives
A fixed-scope pilot is the fastest way to see what our agent can do on a target of your choice — no long-term commitment required. You tell us the disease or indication; we hand back a ranked, evidence-backed shortlist you can take straight to your team.
2–3 weeks
From kickoff to delivered report. Week 1: scoping and data assembly. Weeks 2–3: agent runs, scientific review, and report hand-off.
₹50,000
Fixed fee for a standard single-target pilot. Multi-target and discovery pilots are scoped on request. The fee is creditable toward a larger engagement.
We report performance against held-out ground truth on public datasets — repurposing back-tested on repoDB and ChEMBL, and ADMET/toxicity scored on the open Therapeutics Data Commons (TDC) leaderboard tasks using standard metrics. We share the full evaluation protocol so your team can reproduce every figure before trusting it. See the Performance page for details.
Three things: we deliver both repurposing and de novo discovery in one agentic workflow; every report includes the system's own self-critique (confidence, data gaps, risks) rather than a single marketing number; and we engage through fixed-fee pilots so you can validate the value on your own target before any large commitment.
A ranked, evidence-backed shortlist of candidates for one target disease — each with mechanistic rationale, supporting citations, and known safety/ADMET flags — delivered as a written report plus a walkthrough call, typically in 2–3 weeks. See the pilot details above.
To start, we only need the target disease or indication. Our analysis draws on public scientific databases; any proprietary data you choose to share is used solely for your engagement under a confidentiality agreement and is never used to train shared models.
Yes. Because the pipeline is data-driven and modular, it adapts to rare and under-studied indications — though for very sparse targets the report will clearly flag lower confidence and the specific data gaps, so you know where the evidence is thin.
No — the platform accelerates and de-risks the early work so your scientists spend their time on the highest-value decisions. Every report is designed to be reviewed, challenged, and validated by your team in the lab.
Still have a question? Talk to our team →
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