Loading...
Loading...
Found 22 Skills
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
Identify drug repurposing candidates using ToolUniverse for target-based, compound-based, and disease-driven strategies. Searches existing drugs for new therapeutic indications by analyzing targets, bioactivity, safety profiles, and literature evidence. Use when exploring drug repurposing opportunities, finding new indications for approved drugs, or when users mention drug repositioning, off-label uses, or therapeutic alternatives.
Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery.
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
Open-source cheminformatics and machine learning toolkit for drug discovery, molecular manipulation, and chemical property calculation. RDKit handles SMILES, molecular fingerprints, substructure searching, 3D conformer generation, pharmacophore modeling, and QSAR. Use when working with chemical structures, drug-like properties, molecular similarity, virtual screening, or computational chemistry workflows.
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
Comprehensive computational validation of drug targets for early-stage drug discovery. Evaluates targets across 10 dimensions (disambiguation, disease association, druggability, chemical matter, clinical precedent, safety, pathway context, validation evidence, structural insights, validation roadmap) using 60+ ToolUniverse tools. Produces a quantitative Target Validation Score (0-100) with GO/NO-GO recommendation. Use when users ask about target validation, druggability assessment, target prioritization, or "is X a good drug target for Y?"
Find, characterize, and source small molecules for chemical biology and drug discovery. Covers compound identification (PubChem, ChEMBL), structure search, binding affinity data, ADMET/drug-likeness prediction, and commercial availability (eMolecules, Enamine). Use when asked to find compounds, assess drug-likeness, search by structure, retrieve binding affinities, or source chemicals.
Cheminformatics provides computational chemistry workflows using RDKit for molecular property prediction, virtual screening, ADMET analysis, molecular docking preparation, and chemical space exploration.