adme-property-predictor
Predict ADME (Absorption, Distribution, Metabolism, Excretion) properties
Best for early‑stage drug discoveryWorks with GitHubLow risk
⌘source
- author
@LeoYeAI- language
- Python
★2kstars
306forks
May 6, 2026
✦overview.md
Key Features
- ·Multi‑property ADME prediction
- ·Drug‑likeness scoring (Lipinski, Veber, QED)
- ·Batch processing of compound libraries
- ·Structure‑based liability identification
- ·Candidate ranking by PK profile
Use Cases
- →Screen a compound library for drug‑like properties during early discovery
- →Prioritize lead compounds for advancement based on predicted PK
- →Identify ADME liabilities that require structural optimization
- →Compare analogs to select candidates with optimal ADME profiles
- →Filter virtual screening hits before synthesis
Best for
- ✓early‑stage drug discovery
- ✓lead optimization teams
- ✓virtual screening pipelines
Not ideal for
- !late‑stage clinical candidates
- !biologics or large‑molecule drugs
FAQs
name
adme-property-predictor
description
Predict ADME (Absorption, Distribution, Metabolism, Excretion) properties for drug candidates using cheminformatics models and molecular descriptors. Evaluates drug-likeness, bioavailability, and pharmacokinetic profile to guide lead optimization and candidate selection in drug discovery.
license
MIT
allowed-tools:["Read","Write","Bash","Edit"]
metadata:{"skill-author":"AIPOCH"}
ADME Property Predictor
Overview
Comprehensive pharmacokinetic prediction tool that assesses drug-likeness and ADME properties of small molecules using validated cheminformatics models, molecular descriptors, and structure-property relationships.
Key Capabilities:
- Multi-Property Prediction: Absorption, Distribution, Metabolism, Excretion
- Drug-Likeness Scoring: Lipinski's Rule of 5, Veber rules, QED score
- Batch Processing: Analyze compound libraries efficiently
- Structure-Based Insights: Identify liability hotspots and optimization opportunities
- Comparative Analysis: Rank candidates by predicted PK profile
When to Use
✅ Use this skill when:
- Screening compound libraries for drug-like properties in early discovery
- Prioritizing lead compounds for advancement based on predicted PK
- Identifying ADME liabilities requiring structural optimization
- Comparing analogs to select candidates with optimal ADME profiles
- Filtering virtual screening hits before synthesis
- Generating ADME data for regulatory pre-submission packages
- Teaching pharmacokinetics and drug design principles
❌ Do NOT use when:
$install
1-click copynpx skills add LeoYeAI/openclaw-master-skills --skill adme-property-predictorSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
4/ 5
highMostly actionable with clear steps; only a few small gaps remain.