Optimus Chem - Comprehensive Chemical Analysis Package

Optimus Chem is a comprehensive Python package for molecular property calculations and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) rules analysis. It provides accurate calculations matching Discovery Studio standards for drug discovery applications.

Features

Molecular Property Calculations

• Accurate RDKit-based calculations matching Discovery Studio standards
• Molecular weight, LogP, LogD, pKa predictions
• Hydrogen bond donors/acceptors
• Topological Polar Surface Area (TPSA)
• Rotatable bonds and ring analysis
• Molar refractivity and more

Complete ADMET Rules Analysis

Implementation of all 14 major drug screening rules:

1. Lipinski (Ro5) - Oral bioavailability
2. Veber - Permeability and solubility
3. Ghose - Drug-likeness
4. Egan - Oral absorption
5. Muegge - Broad drug-likeness
6. Rule of 3 - Fragment-based design
7. CNS MPO - CNS exposure potential
8. bRo5 - Non-classical scaffolds
9. PAINS - False positive alerts
10. Pfizer 3/75 - Promiscuity/toxicity alert
11. GSK 4/400 - ADMET risk reduction
12. Lead-likeness - Lead compound transition
13. Brenk filters - Unstable/toxic groups
14. REOS - Rapid compound filtering

🔧 Easy-to-Use API

from optimus import ChemicalAnalyzer

analyzer = ChemicalAnalyzer()
results = analyzer.analyze_smiles("CCO")  # Ethanol
print(results.lipinski_violations)  # 0
print(results.drug_likeness_score)  # 0.85

Installation

pip install optimus-chem

From Source

https://github.com/pritampanda15/Optimus_Chemical_Analyzer
cd Optimus_Chemical_Analyzer
pip install -e .

Quick Start

Basic Analysis

from optimus import ChemicalAnalyzer

# Initialize analyzer
analyzer = ChemicalAnalyzer()

# Analyze a compound
results = analyzer.analyze_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")  # Aspirin

# Access results
print(f"Molecular Weight: {results.molecular_weight:.2f}")
print(f"LogP: {results.logp:.2f}")
print(f"Lipinski Violations: {results.lipinski_violations}")
print(f"Drug-likeness Score: {results.drug_likeness_score:.2f}")

Batch Analysis

smiles_list = [
    "CCO",  # Ethanol
    "CC(=O)OC1=CC=CC=C1C(=O)O",  # Aspirin
    "CN1C=NC2=C1C(=O)N(C(=O)N2C)C"  # Caffeine
]

results = analyzer.analyze_batch(smiles_list)
df = results.to_dataframe()
print(df[['SMILES', 'MW', 'LogP', 'Lipinski_Violations']])

Command Line Interface

Available Commands

optimus --help

Options:

• --version Show the version and exit.
• --help Show this message and exit.

Commands:

• analyze Analyze a single SMILES string.
• batch Analyze multiple compounds from a file.
• report Generate HTML report from analysis results.
• validate Validate a SMILES string.

Command Usage Examples

# Get help for specific commands
optimus analyze --help
optimus batch --help
optimus report --help
optimus validate --help

# Analyze single compound
optimus analyze "CCO"

# Analyze from file
optimus batch compounds.smi --output results.csv

# Generate report
optimus report compounds.smi --format html

# Validate SMILES
optimus validate "CCO"

API Reference

ChemicalAnalyzer Class

Methods

• analyze_smiles(smiles: str) -> AnalysisResult
• analyze_batch(smiles_list: List[str]) -> BatchResult
• analyze_mol(mol: Mol) -> AnalysisResult
• analyze_sdf(sdf_file: str) -> BatchResult

Properties Calculated

• molecular_weight: Molecular weight (Da)
• logp: Partition coefficient
• logd: Distribution coefficient at pH 7.4
• pka: Acid dissociation constant
• hbd: Hydrogen bond donors
• hba: Hydrogen bond acceptors
• tpsa: Topological polar surface area
• rotatable_bonds: Number of rotatable bonds
• aromatic_rings: Number of aromatic rings
• molar_refractivity: Molar refractivity

ADMET Rules

Each rule returns a RuleResult object with:

• passed: Boolean indicating if rule is satisfied
• violations: Number of violations
• details: Detailed breakdown of criteria

HTML Report Generation

Optimus can generate comprehensive HTML reports with interactive visualizations and detailed analysis results:

from optimus import ChemicalAnalyzer

analyzer = ChemicalAnalyzer()

# Analyze multiple compounds
smiles_list = [
    "CCO",  # Ethanol
    "CC(=O)OC1=CC=CC=C1C(=O)O",  # Aspirin
    "CN1C=NC2=C1C(=O)N(C(=O)N2C)C"  # Caffeine
]

# Generate HTML report
analyzer.generate_html_report(smiles_list, output_file="analysis_report.html")

Command Line HTML Report

# Generate HTML report from SMILES file
optimus report compounds.smi --format html --output report.html

The HTML report includes:

• Summary Statistics: Total compounds, success rates, average properties
• Rule Pass Rates: Pass/fail statistics for all 14 ADMET rules
• Individual Results: Detailed breakdown for each compound with color-coded pass/fail indicators
• Interactive Features: Sortable tables and responsive design

Sample HTML Output Features:

• ✅ Pass/Fail Indicators: Green for pass, red for fail, gray for N/A
• Summary Tables: Key metrics and rule compliance rates
• Detailed Analysis: Molecular properties and ADMET rule results
• Responsive Design: Works on desktop and mobile devices

Examples

Drug-likeness Assessment

from optimus import ChemicalAnalyzer
import pandas as pd

analyzer = ChemicalAnalyzer()

# FDA approved drugs
fda_drugs = [
    "CC(=O)OC1=CC=CC=C1C(=O)O",  # Aspirin
    "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",  # Ibuprofen
    "CN1C=NC2=C1C(=O)N(C(=O)N2C)C",  # Caffeine
]

results = []
for smiles in fda_drugs:
    result = analyzer.analyze_smiles(smiles)
    results.append({
        'SMILES': smiles,
        'MW': result.molecular_weight,
        'LogP': result.logp,
        'Lipinski_Pass': result.lipinski.passed,
        'Drug_Score': result.drug_likeness_score
    })

df = pd.DataFrame(results)
print(df)

CNS Drug Analysis

# Analyze CNS penetration potential
result = analyzer.analyze_smiles("CN1C=NC2=C1C(=O)N(C(=O)N2C)C")  # Caffeine

print(f"CNS MPO Score: {result.cns_mpo.score}")
print(f"BBB Permeability: {result.bbb_permeability}")
print(f"P-gp Substrate: {result.pgp_substrate}")

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use Optimus in your research, please cite:

Optimus Chem: Comprehensive Chemical Analysis Package for Drug Discovery
P.K.Panda.et al. (2025)

Support

• 📧 Email: pritam@stanford.edu
• 🐛 Issues: GitHub Issues