Agent skill · research science · k-dense-ai
datamol
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.
Why this skill is useful
Provides domain-specific knowledge for cheminformatics workflows, including molecular handling and parallel processing patterns that the AI wouldn't reliably generate on its own.
What it needs
Requires datamol, gcsfs, s3fs installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 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. Version note: Examples target datamol 0.12.x (PyPI stable: 0.12.5, June 2024). Since 0.10.0, modules are lazy-loaded by default (set DATAMOLDISABLELAZYLOADING=1 to disable). Since 0.12.2, RDKit is a direct PyPI dependency of datamol. Fingerprints use RDKit's rdFingerprintGenerator API (0.12.5+). 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: RDKit is installed automatically with datamol. For remote file paths (S3, GCS, HTTP), install the matching fsspec backend: Import convention: Core Workflows Ten workflow areas, each with worked code, are documented in references/coreworkflows.md: # Area Covers --- --- --- 1 Basic molecule handling tomol, 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 cl …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/datamol