| name | admet-reasoning |
| description | Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026). |
| category | chemistry |
| license | MIT |
| metadata | {"skill-author":"Synthetic Sciences"} |
| version | 1.0.0 |
| tags | ["drug-discovery","ADMET","toxicity","interpretation","reasoning"] |
| dependencies | ["rdkit-pypi","numpy","pandas"] |
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
| Script | Purpose | Key Outputs |
|---|
reason_admet.py | Full ADMET reasoning with structural explanations | JSON report with liabilities, causes, mechanisms, fixes |
