| name | drug-discovery |
| description | Supports drug discovery workflows including target identification, virtual screening, ADMET prediction, lead optimization, pharmacokinetics modeling, and drug repurposing analyses; trigger when users discuss drug targets, compound libraries, medicinal chemistry, or pharmaceutical development. |
When to Trigger
Activate this skill when the user mentions:
- Drug target identification, druggability assessment
- Virtual screening, molecular docking, pharmacophore
- ADMET (absorption, distribution, metabolism, excretion, toxicity)
- Lead optimization, SAR (structure-activity relationship)
- Pharmacokinetics (PK), pharmacodynamics (PD), PK/PD modeling
- Drug repurposing, off-label, drug-disease associations
- SMILES, InChI, compound libraries, chemical fingerprints
- IC50, EC50, Ki, dose-response curves
Step-by-Step Methodology
- Target identification and validation - Identify therapeutic target from literature, GWAS hits, or omics data. Assess druggability using Open Targets, DGIdb, or structural pocket analysis. Confirm target-disease association strength.
- Compound sourcing - Search ChEMBL, PubChem, ZINC, or DrugBank for known active compounds. For novel scaffolds, consider de novo design tools (REINVENT, MolGPT).
- Virtual screening - Structure-based: dock compound library against target (AutoDock Vina, Glide). Ligand-based: use pharmacophore models or molecular fingerprint similarity. Filter by drug-likeness (Lipinski Ro5, Veber rules).
- ADMET prediction - Predict absorption (Caco-2 permeability, logP), distribution (plasma protein binding, Vd), metabolism (CYP inhibition/induction), excretion (clearance), and toxicity (hERG, hepatotoxicity, AMES mutagenicity). Use SwissADME, pkCSM, or ADMETlab.
- Lead optimization - Analyze SAR from dose-response data. Identify key pharmacophoric features. Suggest modifications to improve potency, selectivity, or ADMET profile while maintaining drug-likeness.
- PK/PD modeling - Build compartmental PK models. Estimate key parameters: Cmax, Tmax, AUC, half-life, bioavailability. For PD, model dose-response (Emax model, Hill equation).
- Drug repurposing analysis - Query drug-gene interaction databases. Analyze shared pathways between drug targets and disease mechanisms. Check clinical trial databases for existing evidence.
Key Databases and Tools
- ChEMBL - Bioactivity data for drug-like compounds
- PubChem - Chemical structure and bioassay data
- DrugBank - Drug and target information
- Open Targets - Target-disease associations
- ZINC - Purchasable compound library
- SwissADME / pkCSM - ADMET prediction tools
- BindingDB - Protein-ligand binding data
Output Format
- Compound results as tables: SMILES, molecular weight, logP, key activity (IC50/EC50), ADMET flags.
- Docking results: binding energy (kcal/mol), key interactions, pose description.
- PK parameters: Cmax, Tmax, AUC, t1/2, clearance, bioavailability with units.
- SAR analysis: matched molecular pair comparisons with activity changes.
Quality Checklist
