Best use case
chemistry is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Teams using chemistry should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/chemistry/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How chemistry Compares
| Feature / Agent | chemistry | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill provides specific capabilities for your AI agent. See the About section for full details.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Chemistry & Drug Discovery ## 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 1. Define target (protein structure from PDB) 2. Prepare compound library (from ChEMBL/ZINC/Enamine) 3. Filter by drug-likeness (Lipinski's Rule of Five) 4. Docking (AutoDock Vina, GNINA) 5. Scoring and ranking 6. ADMET prediction (absorption, distribution, metabolism, excretion, toxicity) 7. Hit validation ### QSAR Modeling 1. Curate activity data (ChEMBL IC50/Ki/EC50) 2. Calculate molecular descriptors (RDKit) 3. Feature selection 4. Model training (Random Forest, XGBoost, neural network) 5. Validation (cross-validation, external test set) 6. 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 |
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