drug-discovery

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

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