ADMET Reasoning

Overview

Standard ADMET prediction tools output scores (e.g., "hERG = 0.85") without explaining why. This skill adds mechanistic reasoning — mapping each ADMET liability to its structural cause, the biological mechanism it affects, and a suggested structural fix.

Based on:

• CoTox (Park et al., 2025): Chain-of-thought toxicity reasoning with structural + biological context improved F1 from 0.37 to 0.66
• DrugR (Liu et al., 2026): Explicit liability reasoning before optimization improved scores 18×

When to Use This Skill

• After ADMET prediction: Interpret flagged liabilities with structural explanations
• Lead optimization planning: Understand which structural features to modify and why
• Toxicity reports: Generate interpretable toxicity assessments for medicinal chemistry teams
• Design review: Evaluate whether proposed modifications address the right liabilities

Do NOT use this skill for:

• Raw ADMET score computation (use admet-prediction instead)
• Molecular optimization (use molecular-optimization instead)

Related Skills

• admet-prediction: Compute ADMET properties (run this first)
• molecular-optimization: Iterative optimization using liability analysis
• rdkit: Core molecular operations

Installation

Required dependencies

pip install rdkit-pypi numpy pandas

Core Workflows

1. Full Liability Report

Generate interpretable ADMET analysis for a molecule:

python scripts/reason_admet.py --smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" --output report.json

2. Batch Liability Analysis

python scripts/reason_admet.py --input compounds.csv --output liability_report.csv

3. Targeted Toxicity Reasoning

Focus on specific endpoints:

python scripts/reason_admet.py --smiles "CCN1CCCC1" --endpoints hERG,DILI,CYP --output tox_report.json

Script Reference