rdkit-cheminformatics
Cheminformatics toolkit for molecular analysis and virtual screening: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints (Morgan/ECFP, MACCS), Tanimoto similarity, SMARTS substructure filtering, Lipinski drug-likeness, reaction enumeration, 2D/3D coordinates. For simpler API use datamol; use RDKit for fine-grained sanitization, custom fingerprints, or SMARTS/reaction control.
npx skills add BioTender-max/awesome-bio-agent-skills --skill rdkit-cheminformatics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RDKit Cheminformatics Toolkit ## Overview RDKit is the standard open-source cheminformatics library for Python, providing comprehensive APIs for molecular parsing, descriptor calculation, fingerprinting, substructure searching, and chemical reactions. This skill walks through a complete compound library profiling and virtual screening workflow — from loading molecules through drug-likeness filtering, similarity screening, and result visualization. ## When to Use - Calculate molecular properties (MW, LogP, TPSA, HBD/HBA) for a compound set - Screen a library against a reference compound using fingerprint similarity - Filter compounds by substructure (SMARTS patterns) for functional group analysis - Assess drug-likeness using Lipinski's Rule of Five or custom filters - Generate 2D depictions or 3D conformers for downstream docking - Enumerate chemical libraries using reaction SMARTS (combinatorial chemistry) - Cluster compounds by structural similarity for diversity analysis - Standardize and deduplicate molecular datasets (canonical SMILES, InChI) - Use `datamol-cheminformatics` instead for a higher-level RDKit wrapper with batching and error handling; use `openbabel` instead for
- Overview
- When to Use
- Prerequisites
- Workflow
- Step 1: Load and Validate Molecules
- Step 2: Standardize and Deduplicate
- Step 3: Calculate Molecular Descriptors
- Step 4: Apply Drug-Likeness Filters
- Step 5: Generate Fingerprints and Similarity Search
- Step 6: Substructure Filtering with SMARTS
- Step 7: 2D Visualization and Grid Plots
- Step 8: Export Results
- Key Parameters
- Common Recipes
Option 1: pip (lightweight) pip install rdkit-pypi pandas matplotlib numpy Option 2: conda (full features including cartridge) conda install -c conda-forge rdkit pandas matplotlib numpy
What does the rdkit-cheminformatics skill do?
Cheminformatics toolkit for molecular analysis and virtual screening: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints (Morgan/ECFP, MACCS), Tanimoto similarity, SMARTS substructure filtering, Lipinski drug-likeness, reaction enumeration, 2D/3D coordinates. For simpler API use datamol; use RDKit for fine-grained sanitization, custom fingerprints, or SMARTS/reaction control.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill rdkit-cheminformatics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.