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admet-reasoning

Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).

The English summary is being prepared. Showing the original description.

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Files included (2)

  • SKILL.md2.5 KB
  • scripts/reason_admet.py11.2 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

ScriptPurposeKey Outputs
reason_admet.pyFull ADMET reasoning with structural explanationsJSON report with liabilities, causes, mechanisms, fixes

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