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admet-prediction

ADMET property prediction for drug candidates. Full pharmacokinetic panel (Caco-2, PPB, clearance, CYP), toxicity (hERG, AMES, DILI), drug-likeness (Lipinski, QED), using RDKit descriptors and TDC models.

backend/cli/skills/chemistry/admet-prediction/SKILL.md

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# ADMET Property Prediction

## Overview

ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling is one of the most critical stages in the drug discovery pipeline. Poor pharmacokinetic and toxicity properties are responsible for roughly 40% of clinical trial failures. Computational ADMET prediction enables medicinal chemists to triage compounds early, prioritize synthesis efforts, and design molecules with improved drug-like profiles before committing to expensive in vitro and in vivo studies.

This skill provides a comprehensive suite of ADMET prediction tools built on RDKit molecular descriptors, validated SMARTS-based structural alert libraries, and established empirical models. Every prediction is accompanied by a traffic-light classification (GREEN / YELLOW / RED) so that results can be interpreted at a glance.

## When to Use This Skill

- **Hit-to-lead optimization**: Rank hits from a high-throughput screen by their predicted ADMET profile.
- **Lead optimization**: Identify liabilities in a lead series and guide structural modifications.
- **Virtual screening triage**: Filter large compound libraries before docking or ML scoring.
- **Candidate comparison**: Compare your candidates head-to-head against each other or against FDA-approved drug statistics.