tools-only Molecular Descriptors - Usage Guide
Overview
Calculate molecular fingerprints and physicochemical properties for compound characterization, drug-likeness assessment, and machine learning featurization.
Prerequisites
pip install rdkit
pip install numpy pandas
Quick Start
Tell your AI agent what you want to do:
- "Calculate ECFP4 fingerprints for my compound library"
- "Check Lipinski rule of 5 compliance for these molecules"
- "Calculate QED drug-likeness scores"
- "Generate descriptors for machine learning"
Example Prompts
Fingerprints
"Generate Morgan fingerprints with radius 2 and 2048 bits for my molecules."
"Calculate MACCS keys for similarity searching."
Drug-Likeness
"Check which compounds pass Lipinski's rule of 5."
"Calculate QED scores and filter for drug-like compounds (QED > 0.5)."
Full Descriptor Set
"Calculate all available RDKit descriptors for my molecules."
"Generate 3D conformers and calculate shape descriptors."
What the Agent Will Do
- Load molecules from provided structures
- Calculate requested descriptors/fingerprints
- Compile results into a DataFrame
- Apply filters based on thresholds if requested
- Export results in requested format
Tips
- ECFP4 = radius 2, ECFP6 = radius 3 (diameter = 2 * radius + 2)
- Include
useChirality=Truefor stereo-sensitive fingerprints - QED > 0.5 is generally considered drug-like
- ETKDGv3 is now the default conformer generator in RDKit
- Lipinski thresholds: MW <= 500, LogP <= 5, HBD <= 5, HBA <= 10
- 3D descriptors require conformer generation first
Related Skills
- molecular-io - Load molecules for descriptor calculation
- similarity-searching - Use fingerprints for similarity
- admet-prediction - Predict ADMET from descriptors
- machine-learning/biomarker-discovery - ML on molecular features