Agent skill · research science · aiskillstore
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Why this skill is useful
Provides domain-specific commands for cheminformatics operations that the AI wouldn't reliably generate on its own.
What it needs
Requires datamol installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Datamol Cheminformatics Skill Overview Datamol is a Python library that provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. Simplify complex molecular operations with sensible defaults, efficient parallelization, and modern I/O capabilities. All molecular objects are native rdkit.Chem.Mol instances, ensuring full compatibility with the RDKit ecosystem. Key capabilities: Molecular format conversion (SMILES, SELFIES, InChI) Structure standardization and sanitization Molecular descriptors and fingerprints 3D conformer generation and analysis Clustering and diversity selection Scaffold and fragment analysis Chemical reaction application Visualization and alignment Batch processing with parallelization Cloud storage support via fsspec Installation and Setup Guide users to install datamol: Import convention: Core Workflows 1. Basic Molecule Handling Creating molecules from SMILES: Converting molecules to SMILES: Standardization and sanitization (always recommend for user-provided molecules): 2. Reading and Writing Molecular Files Refer to references/iomodule.md for comprehensive I/O documentation. Reading files: Writing files: Remote file support (S3, GCS, HTTP): 3. Molecular Descriptors and Properties Refer to references/descriptorsviz.md for detailed descriptor documentation. Computing descriptors for a single molecule: Batch descriptor computation (recommended for datasets): Specific descriptors: Drug-likeness filtering (Lipinski's Rule of Five): 4. Molecular Fingerprints and Similarity Generating fingerprints: Similarity calculations: 5. Clustering and Diversity Selection Refer to references/coreapi.md for clustering details. Butina clustering: Important: Butina clustering builds a full distance matrix - suitable for ~1000 molecules, not for 10,000+. Diversity selection: 6. Scaffold Analysis Refer to references/fragmentsscaffolds.md for complete scaffold documentation. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/datamol