compound-profile

name	compound-profile
description	Generate comprehensive compound profiles including structure, properties, bioactivity, and development status. Use for drug analysis, SAR studies, and competitive profiling. Keywords: compound, drug, molecule, structure, SMILES, bioactivity, IC50
category	Compound Analysis
tags	["compound","drug","structure","bioactivity","chembl"]
version	1.0.0
author	Drug Discovery Team
dependencies	["chembl-database","pubchem-database","drugbank-database"]

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

• ChEMBL API Reference
• PubChem API
• Property Calculation

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

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