Chemistry & Drug Discovery
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Then ask your AI: use the Chemistry & Drug Discovery skill
What this skill tells your AI
The instructions your AI receives, as published by zaoqu-liu/scienceclaw in skills/chemistry/SKILL.md and read by ahel’s review.
Overview
Computational chemistry, cheminformatics, and drug discovery workflows.
Key Tools
- RDKit: Molecular manipulation, fingerprints, descriptors, substructure search
- PubChem: Chemical compound database (100M+ compounds)
- ChEMBL: Bioactivity database for drug-like molecules
- Open Babel: Format conversion, 3D generation
- AutoDock Vina: Molecular docking
- GROMACS/OpenMM: Molecular dynamics simulations
Common Workflows
Virtual Screening
- Define target (protein structure from PDB)
- Prepare compound library (from ChEMBL/ZINC/Enamine)
- Filter by drug-likeness (Lipinski's Rule of Five)
- Docking (AutoDock Vina, GNINA)
- Scoring and ranking
- ADMET prediction (absorption, distribution, metabolism, excretion, toxicity)
- Hit validation
QSAR Modeling
- Curate activity data (ChEMBL IC50/Ki/EC50)
- Calculate molecular descriptors (RDKit)
- Feature selection
- Model training (Random Forest, XGBoost, neural network)
- Validation (cross-validation, external test set)
- Applicability domain assessment
Molecular Property Prediction
- Lipophilicity (LogP)
- Solubility (LogS)
- Permeability (PAMPA, Caco-2)
- Metabolic stability (CYP inhibition)
- hERG toxicity
- BBB penetration
Databases
| Database | Content | Access |
|---|---|---|
| PubChem | 100M+ compounds | Free API |
| ChEMBL | Bioactivity data | Free API |
| PDB | 200K+ protein structures | Free API |
| ZINC | Purchasable compounds | Free download |
| DrugBank | Drug information | Free academic |
| Materials Project | Inorganic materials | Free API |
Signals
- GitHub stars
- 60
- Forks
- 14
- Last commit
- Mar 2026
Advanced
- Catalog kind
- skill
- Gateway key
chemistry-zaoqu-liu- Source
- github.com/zaoqu-liu/scienceclaw