Chemistry & Drug Discovery

## Overview

912 stars

Best use case

Chemistry & Drug Discovery is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

## Overview

Teams using Chemistry & Drug Discovery 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

$curl -o ~/.claude/skills/chemistry/SKILL.md --create-dirs "https://raw.githubusercontent.com/wu-yc/LabClaw/main/skills/pharma/chemistry/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/chemistry/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Chemistry & Drug Discovery Compares

Feature / AgentChemistry & Drug DiscoveryStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

## Overview

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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