Claude-skill-registry compound-profile
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/compound-profile" ~/.claude/skills/majiayu000-claude-skill-registry-compound-profile && rm -rf "$T"
manifest:
skills/data/compound-profile/SKILL.mdsource content
Compound Profile Skill
Comprehensive compound analysis for drug discovery and medicinal chemistry.
Quick Start
/compound erlotinib /compound-profile CC1=CC=C(C=C1)CNC(=O)C1=NC=C(C=C1)N Analyze osimertinib properties and bioactivity Compare gefitinib, erlotinib, afatinib profiles
What's Included
| Section | Description | Data Source |
|---|---|---|
| Basic Info | Name, type, status, company | ChEMBL, DrugBank |
| Structure | SMILES, InChI, molecular weight | PubChem, ChEMBL |
| Properties | LogP, HBD, HBA, TPSA, RO5 | Calculated, PubChem |
| Bioactivity | Target affinity, IC50, Ki | ChEMBL, BindingDB |
| Development | Phase, indications, status | Drugs@FDA |
| Similar Compounds | Structure similarity search | ChEMBL |
| Safety | Known toxicity, warnings | SIDER, PubChem |
Output Structure
# Compound Profile: Erlotinib ## Executive Summary Erlotinib is a first-generation EGFR TKI approved for NSCLC (2004). Key characteristics: Oral bioavailability, good brain penetration, resistance mutations limit long-term efficacy. ## Basic Information | Field | Value | |-------|-------| | Name | Erlotinib | | Brand Names | Tarceva | | ChEMBL ID | CHEMBL880 | | Type | Small molecule | | Class | Kinase inhibitor | | Status | Approved | | Approval Year | 2004 | | Company | Astellas (OSI) | | Indications | NSCLC, pancreatic cancer | ## Structure & Properties **SMILES:** `COc1cc2nc(Nc3ccc(Oc4ccc(O)cc4)cc3)nc2cc1OC` | Property | Value | Rule of 5 Check | |----------|-------|----------------| | MW | 393.4 Da | ✓ (<500) | | LogP | 3.1 | ✓ (<5) | | HBD | 1 | ✓ (≤5) | | HBA | 7 | ✓ (≤10) | | TPSA | 76.3 Ų | ✓ (<140) | | Rotatable Bonds | 6 | | ## Bioactivity | Target | Type | Affinity | Units | |--------|------|----------|-------| | EGFR | IC50 | 0.5 | nM | | ERBB2 | IC50 | 1200 | nM | | LCK | IC50 | 5 | nM | ## Development History | Year | Milestone | |------|-----------| | 2004 | FDA Approval (NSCLC) | | 2005 | EMEA Approval | | 2010 | Pancreatic cancer approval | | 2011 | Generic launch (US) | ## Similar Compounds | Compound | Similarity | Difference | |----------|------------|------------| | Gefitinib | 85% | Different core scaffold | | Afatinib | 72% | Irreversible binder | | Osimertinib | 68% | 3rd-gen, mutant-selective | | Icotinib | 82% | China-approved analog | ## Safety Profile **Common AEs:** Rash, diarrhea, fatigue, anorexia **Boxed Warning:** Interstitial lung disease **Contraindications:** Hypersensitivity to erlotinib ## Patent Status | Patent | Number | Expiry | |--------|---------|--------| | Base patent | US5747498 | 2019 (expired) | | Formulation | US6943129 | 2020 | | Method of use | US6900221 | 2021 |
Examples
By Name
/compound erlotinib /compound-profile sotorasib
By Structure
/compound "CC1=CC=C(C=C1)CNC(=O)C1=NC=C(C=C1)N" /compound-profile SMILES
Comparison
Compare compounds erlotinib, gefitinib, afatinib Analyze bioactivity across EGFR inhibitors
Property Analysis
/compound erlotinib --focus properties Analyze drug-likeness of this compound Check Lipinski rule of 5 violations
Running Scripts
# Fetch compound by name python scripts/fetch_compound_data.py erlotinib --output compound.json # Fetch by SMILES python scripts/fetch_compound_data.py --smiles "CC1=CC=C..." -o data.json # Similarity search python scripts/fetch_compound_data.py --similar CHEMBL880 --threshold 0.7 # Bioactivity summary python scripts/fetch_compound_data.py erlotinib --bioactivity -o activity.json # Structure search python scripts/fetch_compound_data.py --scaffold quinazoline --limit 20
Requirements
pip install requests pandas rdkit
Additional Resources
Best Practices
- Use standard names: Generic names preferred over brand
- Verify ChEMBL ID: Most reliable identifier
- Check bioactivity: Cross-reference multiple sources
- Analog analysis: Use similarity searches for SAR
- Validate SMILES: Check structure validity
Common Pitfalls
| Pitfall | Solution |
|---|---|
| Name ambiguity | Use ChEMBL ID when possible |
| Stereochemistry | SMILES may not capture isomerism |
| Outdated data | Check multiple sources |
| Salt forms | API may have multiple entries |
| Tautomerism | Different SMILES for same structure |