datamol-cheminformatics
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
npx skills add BioTender-max/awesome-bio-agent-skills --skill datamol-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.
# Datamol Cheminformatics Toolkit ## Overview Datamol provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. It simplifies common drug discovery operations — SMILES parsing, standardization, descriptors, fingerprints, clustering, scaffolds, conformers, and visualization — with sensible defaults, built-in parallelization, and cloud storage support via fsspec. All molecular objects are native `rdkit.Chem.Mol` instances, ensuring full RDKit compatibility. ## When to Use - Parsing, validating, and standardizing molecular structures from SMILES, SDF, or other formats - Computing molecular descriptors and fingerprints for ML featurization - Similarity searching and diversity selection from compound libraries - Clustering compounds by structural similarity (Butina clustering) - Scaffold analysis and scaffold-based train/test splitting for ML - BRICS/RECAP molecular fragmentation for fragment-based design - 3D conformer generation and analysis - Visualizing molecules as grids with alignment and highlighting - Batch processing molecular datasets with parallelization - For quick gene lookups use **gget** instead; for advanced substructure queries or cust
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- 1. Molecular I/O & Standardization
- 2. Descriptors & Properties
- 3. Fingerprints & Similarity
- 4. Clustering & Diversity Selection
- 5. Scaffolds & Fragments
- 6. 3D Conformers
- Key Concepts
- Datamol vs RDKit Decision Guide
- Key Data Types
uv pip install datamol
What does the datamol-cheminformatics skill do?
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill datamol-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 144 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.