drug-discovery

## 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 1. **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. 2. **Compound sourcing** - Search ChEMBL, PubChem, ZINC, or DrugBank for known active compounds. For novel scaffolds, consider de novo design tools (REINVENT, MolGPT). 3. **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). 4. **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. 5. **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. 6. **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). 7. **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 - [ ] Target-disease association supported by evidence (genetic, functional) - [ ] Drug-likeness filters applied (Lipinski, Veber, PAINS) - [ ] ADMET predictions include confidence levels or applicability domain - [ ] Docking validated against known co-crystal structures when available - [ ] IC50/EC50 reported with assay conditions and confidence intervals - [ ] PK parameters include units and species (human vs. preclinical) - [ ] Known liabilities (hERG, CYP inhibition, reactive metabolites) flagged - [ ] Comparison to existing drugs/compounds for the same target included