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.

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:

uv pip install "datamol==0.13.0"

RDKit and the S3/GCS, Excel/Parquet, visualization and SELFIES dependencies are installed by the current distribution; separate feature extras are not required. Use an isolated environment to avoid conflicting scientific-package requirements.

Import convention:

import datamol as dm

Core Workflows

Ten workflow areas, each with worked code, are documented in references/core_workflows.md:

Three end-to-end pipelines — load/filter/analyze, SAR by scaffold series, and virtual screening — are in references/workflow_patterns.md.

Parallelization

Datamol includes built-in parallelization for many operations. Use n_jobs parameter:

• n_jobs=1: Sequential (no parallelization)
• n_jobs=-1: Use all available CPU cores
• n_jobs=4: Use 4 cores

Functions supporting parallelization:

• dm.read_sdf(..., n_jobs=-1)
• dm.descriptors.batch_compute_many_descriptors(..., n_jobs=-1, batch_size=128)
• dm.cluster_mols(..., n_jobs=-1)
• dm.pdist(..., n_jobs=-1)
• dm.conformers.sasa(..., n_jobs=-1)

Progress bars: Many batch operations support progress=True parameter. For parallel descriptor batches, supply a positive batch_size (for example 128). In the tested 0.13.0 stack the default None reaches joblib and raises when parallelism is enabled.

Reference Documentation

For detailed API documentation, consult these reference files:

• references/core_api.md: Core namespace functions (conversions, standardization, fingerprints, clustering)
• references/io_module.md: File I/O operations (read/write SDF, CSV, Excel, remote files)

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