Rdkit

RDKit Cheminformatics Toolkit

Overview

RDKit is a comprehensive cheminformatics library providing Python APIs for molecular analysis and manipulation. This skill provides guidance for reading/writing molecular structures, calculating descriptors, fingerprinting, substructure searching, chemical reactions, 2D/3D coordinate generation, and molecular visualization. Use this skill for drug discovery, computational chemistry, and cheminformatics research tasks.

Checked against: RDKit 2026.03.5 (rdkit 2026.3.5 on PyPI, released 2026-08-03), August 2026. Official installation docs continue to recommend conda-forge for most users, while cross-platform PyPI wheels are published under the rdkit package name. rdkit-pypi is the old PyPI package name and should only appear when maintaining legacy environments.

Installation and Setup

Use uv when installing into an existing Python environment:

uv pip install rdkit

For reproducible chemistry environments, especially when mixing compiled scientific packages, conda-forge remains the upstream recommendation:

conda create -c conda-forge -n my-rdkit-env rdkit
conda activate my-rdkit-env

Avoid installing both conda rdkit and PyPI rdkit/rdkit-pypi into the same environment unless you are deliberately debugging packaging behavior. Mixed installs can make it unclear which binary extension is being imported.

Core Capabilities

Twelve capability areas, each with worked code, are documented in references/core_capabilities.md:

Worked workflows and the performance, thread-safety, and version-sensitivity notes are in references/workflows_and_best_practices.md.

Prefer portable exchange formats (SMILES, SDF) for shared data; for local caches RDKit's binary molecule representation avoids generic pickle.

Common Pitfalls

1. Forgetting to check for None: Always validate molecules after parsing
2. Sanitization failures: Use DetectChemistryProblems() to debug
3. Missing hydrogens: Use AddHs() when calculating properties that depend on hydrogen
4. 2D vs 3D: Generate appropriate coordinates before visualization or 3D analysis
5. SMARTS matching rules: Remember that unspecified properties match anything
6. Thread safety with MolSuppliers: Don't share supplier objects across threads

Resources

references/

All five bundled reference documents, loaded only when needed:

• references/core_capabilities.md - the twelve capability areas above, each with worked code
• references/workflows_and_best_practices.md - end-to-end workflows plus performance, thread-safety, and version-sensitivity notes
• references/api_reference.md - RDKit modules, functions, and classes organized by functionality
• references/descriptors_reference.md - the available molecular descriptors with descriptions
• references/smarts_patterns.md - SMARTS patterns for functional groups and structural features

Only the files listed in references/ and scripts/ are bundled local resources. Names such as rdkit, datamol, scipy, and sklearn refer to installable Python packages, not local files in this skill.

scripts/

# Descriptors for one molecule, or a whole file to CSV
python skills/rdkit/scripts/molecular_properties.py "CC(=O)Oc1ccccc1C(=O)O"
python skills/rdkit/scripts/molecular_properties.py --file library.smi --output properties.csv

# Fingerprint similarity screen; --method morgan|rdkit|maccs|atompair|torsion
python skills/rdkit/scripts/similarity_search.py "c1ccccc1O" library.sdf --threshold 0.6

# SMARTS filtering, with predefined libraries via --list-patterns
python skills/rdkit/scripts/substructure_filter.py library.smi --pattern "C(=O)[OH]" --report hits.csv

Each exits nonzero and writes to stderr on failure, so they compose in a pipeline. They are equally usable as templates for custom workflows.

Composing with the rest of the bundle

• datamol → instead: the same standardization, clustering and parallel work with sensible defaults. Reach for rdkit only when you need control datamol does not expose.
• medchem → after: real triage. Its rule catalogue and the full PAINS/NIBR alert sets are what you want for library filtering; substructure_filter.py here is a general SMARTS tool, not a curated alert set.
• molfeat → after: turning molecules into model-ready features rather than hand-rolled fingerprints.
• chembl → before: measured bioactivity to featurize, rather than a library you invented.
• chemical-space → after: once a SMARTS query defines the chemotype, find purchasable examples.
• admet-prediction / deepchem / pytdc → after: descriptors and fingerprints from here are the input those models expect. Desalt and standardise first or you predict on the wrong species.