Drug Discovery
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
drug-discovery is a Claude skill that guides drug discovery workflows including target identification, virtual screening, ADMET prediction, lead optimization and PK/PD modeling.
About
This skill supports drug discovery workflows from target identification through lead optimization and PK/PD modeling. A researcher uses it for virtual screening, ADMET prediction, SAR analysis and drug repurposing, drawing on databases like ChEMBL, PubChem and DrugBank. It provides a step-by-step methodology and a quality checklist for reporting results.
- Guides target identification, virtual screening, ADMET, lead optimization and PK/PD modeling
- Names key databases and tools: ChEMBL, PubChem, DrugBank, Open Targets, ZINC, SwissADME
- Includes a quality checklist covering druggability, drug-likeness filters and known liabilities
Drug Discovery by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,318 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
drug-discovery capabilities & compatibility
- Capabilities
- drug discovery · virtual screening · admet prediction · sar analysis
- Use cases
- research · data analysis
What drug-discovery says it does
Supports drug discovery workflows including target identification, virtual screening, ADMET prediction, lead optimization, pharmacokinetics modeling, and drug repurposing analyses
Predict absorption (Caco-2 permeability, logP), distribution (plasma protein binding, Vd), metabolism (CYP inhibition/induction), excretion (clearance), and toxicity
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Run cheminformatics drug-discovery steps: target ID, virtual screening, ADMET, lead optimization and PK/PD.
Who is it for?
Cheminformatics drug-discovery analysis: screening, ADMET, SAR, PK/PD and repurposing.
Skip if: Pure protein structure analysis or single-compound lookups without a discovery workflow.
When should I use this skill?
A user discusses drug targets, compound libraries, medicinal chemistry or ADMET.
What you get
Compound tables, docking results, PK parameters and SAR analysis with a quality checklist applied.
- Compound results tables
- Docking results
- PK parameters
By the numbers
- 7-step discovery methodology
- 8-item quality checklist
Files
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
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Which databases does it use?
ChEMBL, PubChem, DrugBank, Open Targets, ZINC, SwissADME/pkCSM and BindingDB.
What steps does it cover?
Target identification, compound sourcing, virtual screening, ADMET prediction, lead optimization, PK/PD modeling and repurposing.