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.md
source 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

SectionDescriptionData Source
Basic InfoName, type, status, companyChEMBL, DrugBank
StructureSMILES, InChI, molecular weightPubChem, ChEMBL
PropertiesLogP, HBD, HBA, TPSA, RO5Calculated, PubChem
BioactivityTarget affinity, IC50, KiChEMBL, BindingDB
DevelopmentPhase, indications, statusDrugs@FDA
Similar CompoundsStructure similarity searchChEMBL
SafetyKnown toxicity, warningsSIDER, 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

  1. Use standard names: Generic names preferred over brand
  2. Verify ChEMBL ID: Most reliable identifier
  3. Check bioactivity: Cross-reference multiple sources
  4. Analog analysis: Use similarity searches for SAR
  5. Validate SMILES: Check structure validity

Common Pitfalls

PitfallSolution
Name ambiguityUse ChEMBL ID when possible
StereochemistrySMILES may not capture isomerism
Outdated dataCheck multiple sources
Salt formsAPI may have multiple entries
TautomerismDifferent SMILES for same structure