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Found 26 Skills
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
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.
Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.
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.
Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.
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.
Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
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.
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.