name: datamol description: Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including 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. license: Apache-2.0 license allowed-tools: Read Write Edit Bash compatibility: Requires Python 3.11+ and datamol 0.13.0 with RDKit 2024.09+. Installation needs network access; local molecular workflows need no credentials. Remote I/O needs the selected provider credentials. metadata: version: "1.4" last-reviewed: "2026-09-30" upstream-version: "0.13.0" skill-author: K-Dense Inc.

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.

Verified target: datamol 0.13.0, released September 9, 2026. Local checks used Python 3.13 and RDKit 2026.03.6. Python 3.11+ and RDKit 2024.09+ are required. The 0.13 distribution includes SELFIES, visualization, cloud, Excel and Parquet runtime dependencies. Current API examples and verification limits are recorded in references/review.md. Pin the environment: RDKit upgrades can change canonical representations and retained conformers. Compare chemical invariants, not a fixed conformer count or an exact version-dependent canonical string.

#	Area	Covers
1	Basic molecule handling	`to_mol`, batch conversion, error handling, canonical and isomeric SMILES, sanitization and full standardization
2	Reading and writing files	SDF, SMILES, CSV, Excel with rendered structures, the universal reader/writer, and cloud or HTTPS paths
3	Descriptors and properties	the standard descriptor set, parallel computation, aromaticity, stereochemistry, flexibility, and filtering
4	Fingerprints and similarity	ECFP4 and other types, pairwise and cross-set distances, nearest-neighbour lookup (Tanimoto distance = 1 − similarity)
5	Clustering and diversity	similarity clustering, diverse subset picking, and cluster centroids
6	Scaffold analysis	Bemis-Murcko scaffolds, grouping and counting, and scaffold-disjoint train/test splits
7	Fragmentation	fragmenting molecules, finding common fragments across a library, and fragment-based scoring
8	3D conformers	generation, access, RMSD clustering, representative selection, and SASA
9	Visualization	grids, files, publication SVG, substructure alignment, atom and bond highlighting, conformer display
10	Chemical reactions	reaction SMARTS, applying to a molecule or a whole library

datamol

Install

Related skills

Datamol Cheminformatics Skill

Overview

Installation and Setup

Core Workflows

Parallelization

Reference Documentation

Best Practices

Error Handling

Integration with Machine Learning

Troubleshooting

Additional Resources

Citing Scientific Agent Skills