Stress-test the target before you commit
A weakly-validated target survives to docking and wastes the whole run. NovoMCP challenges every hypothesis against clinical trials, measured bioactivity, and contradicting literature — and returns a confidence tier with the evidence that argues against it, not just the evidence that argues for.
“Validate EGFR as a target for non-small cell lung cancer — and show me the contradicting evidence too.”
How it works
Name the target
Pass a gene or target from the discovery stage. The engine assembles every evidence stream it can find — clinical trials, ChEMBL bioactivity, literature, and the omics layer.
Adversarial weighting
Evidence is combined with tiered weights: clinical trials 3×, ChEMBL bioactivity 2×, literature 1×, omics 1×. Failed trials and contradicting reports are surfaced, not hidden, so a fragile hypothesis fails here instead of at docking.
A confidence tier, with caveats
You receive a tiered confidence assessment and the specific evidence behind it — supporting and contradicting — so the decision to spend compute is made with eyes open.
Proof
validate_target stress-tests each hit with tiered weighting: clinical trials (3×), ChEMBL bioactivity (2×), literature (1×), omics (1×).
Cross-checked against search_clinical_trials, search_chembl (2.4M bioactives), and semantic literature search across 14,000 papers.
Contradicting evidence is weighted in, not filtered out — the goal is to break a target here, cheaply, rather than in a trial.
Use this when you need to
Confirm a target is worth the compute before committing it
Surface failed trials and contradicting evidence early
Rank competing target hypotheses by weighted evidence
Document why a target advanced — or did not — for the audit trail
Break the weak targets here, not in the clinic.
Five evidence streams, weighted adversarially. The contradicting evidence, surfaced before you spend.