Stop missing critical research signals
The complete picture lives in five databases - most teams check two. One query returns papers, preprints, patents, clinical trials, and bioactive compounds - synthesized in context, not dumped as raw exports. Avoid pursuing targets that already failed in trials. Identify overlooked opportunities faster.
“Give me the complete landscape for EGFR inhibition in lung cancer - literature, active trials, known actives, and IP.”
How it works
Name the target or topic
Type a gene, disease, mechanism, or compound. The engine fans out across five sources simultaneously - Pinecone semantic search for papers and patents, live APIs for bioRxiv, ChEMBL, and ClinicalTrials.gov.
Contradictions, gaps, and opportunities - surfaced automatically
Your AI identifies conflicting findings, missing evidence, crowded IP landscapes, and overlooked targets across all five sources. Not five export files to reconcile - one synthesis that prevents bad decisions.
Seed compounds identified
ChEMBL hits with known activity against your target surface as starting scaffolds for lead optimization - with IC50/EC50 data and assay metadata.
Proof
Pinecone semantic search with text-embedding-3-large (3072 dimensions). 14,398 curated drug discovery papers. 2,416 USPTO pharmaceutical patents.
Live API queries to bioRxiv/medRxiv (250K+ preprints), ChEMBL (2.4M bioactive compounds), and ClinicalTrials.gov (500K+ trials).
Five tools: search_literature, search_biorxiv, search_chembl, search_patents, search_clinical_trials.
Use this when you need to
Avoid pursuing targets that already failed in trials
Assess the IP landscape before committing to a scaffold
Find starting compounds with known activity data
Identify gaps in existing research - not just what exists
Don't waste months on the wrong direction
Five databases. One query. One synthesis. Sign up and see the full picture.