tools-only ADMET Prediction - Usage Guide
Overview
Predict absorption, distribution, metabolism, excretion, and toxicity properties for drug discovery. Includes drug-likeness filtering and structural alerts.
Prerequisites
pip install rdkit requests
pip install deepchem # For ML-based predictions
Quick Start
Tell your AI agent what you want to do:
- "Predict ADMET properties for my lead compounds"
- "Filter my library for drug-like compounds"
- "Check for PAINS alerts in my hit list"
- "Predict hERG liability for my compounds"
Example Prompts
ADMET Prediction
"Use ADMETlab 3.0 to predict ADMET properties for these SMILES."
"Predict CYP inhibition profiles for my lead series."
Drug-Likeness
"Calculate Lipinski violations and QED scores for my compounds."
"Filter for compounds passing both Lipinski and Veber rules."
Safety Filtering
"Check my hits for PAINS and other structural alerts."
"Identify compounds with potential hERG liability."
What the Agent Will Do
- Calculate drug-likeness properties (Lipinski, QED)
- Call ADMETlab 3.0 API for predictions
- Filter for PAINS and structural alerts
- Rank compounds by safety profile
- Generate summary report
Tips
- ADMETlab 3.0 provides 119 endpoints (use this, not ADMETlab 2.0)
- SwissADME has NO API - it is web-only, do not try programmatic access
- DeepChem supports both PyTorch and TensorFlow (TF not deprecated)
- QED > 0.5 is generally drug-like
- hERG IC50 > 10 μM is typically considered safe
- PAINS filter removes promiscuous compounds that cause assay interference
Related Skills
- molecular-descriptors - Calculate descriptors for ML
- substructure-search - Filter reactive groups
- virtual-screening - Screen after ADMET filtering