RDKit Cheminformatics Toolkit
FreeAdvanced molecular analysis and manipulation for chemists.
Free · Opens the source repo
What RDKit Cheminformatics Toolkit does
RDKit is a powerful cheminformatics toolkit designed for molecular analysis and manipulation, providing a rich set of Python APIs. This skill allows users to read and write molecular structures in various formats such as SMILES and SDF, calculate molecular descriptors, perform substructure searches, and generate both 2D and 3D molecular coordinates. It is particularly useful for tasks in drug discovery and computational chemistry, enabling detailed molecular control and analysis.
The toolkit supports a variety of functionalities including the calculation of important molecular properties like molecular weight (MW), logP, and topological polar surface area (TPSA). Users can also generate molecular fingerprints for similarity searches and utilize SMARTS patterns for substructure queries. With RDKit, researchers can perform chemical reaction simulations and visualize molecular structures, making it an essential tool for cheminformatics research.
RDKit is suitable for users who require advanced control over molecular data and workflows. It is ideal for computational chemists and researchers who need to implement specialized algorithms or custom sanitization processes. For simpler workflows, users may consider the datamol wrapper, but RDKit provides the depth and flexibility needed for more complex tasks.
The skill includes example scripts for common workflows, such as calculating molecular properties, conducting similarity searches, and filtering molecules by substructure patterns. It also offers extensive documentation and references to guide users through its capabilities, ensuring that both novice and experienced users can effectively leverage its functionalities.
When to use it
Use RDKit when you need detailed molecular control for applications in drug discovery, computational chemistry, or cheminformatics research.
When not to use it
If your needs are limited to basic molecular tasks or if you prefer a simpler interface, consider using the `datamol` wrapper instead.
What you can build with it
Drug Discovery
Utilize RDKit to analyze molecular properties and perform similarity searches to identify potential drug candidates.
Chemical Reaction Simulations
Leverage RDKit's capabilities to simulate chemical reactions and analyze the resulting molecular structures.
Molecular Visualization
Generate 2D and 3D visualizations of molecules to aid in presentations or publications.
How to install RDKit Cheminformatics Toolkit
View source1. Install with the skills CLI
npx skills add k-dense-ai/scientific-agent-skills/rdkit --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by k-dense-aiRDKit 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.
Current baseline (checked 2026-06-07): RDKit 2026.03.3 is the latest GitHub/PyPI release (rdkit 2026.3.3 on PyPI). 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:
| # | Area | Covers |
|---|---|---|
| 1 | Molecular I/O and creation | SMILES, MOL files and blocks, InChI, SDF and SMILES suppliers, multithreaded reading, writers |
| 2 | Sanitization and validation | disabling automatic sanitization, manual and partial sanitization, detecting problems first |
| 3 | Analysis and properties | atom and bond iteration, ring information and SSSR, chirality and stereochemistry, fragments |
| 4 | Descriptors | MW, LogP, TPSA, H-bond donors/acceptors, rotatable bonds, aromatic rings, bulk calculation, drug-likeness |
| 5 | Fingerprints and similarity | topological, Morgan/ECFP via rdFingerprintGenerator, MACCS, atom pair, torsion, Avalon; Tanimoto and other metrics; Butina clustering |
| 6 | Substructure searching | SMARTS queries, match retrieval, and a library of common patterns |
| 7 | Chemical reactions | reaction SMARTS, applying reactions, reaction fingerprints |
| 8 | 2D and 3D coordinates | depiction, template alignment, ETKDG embedding, force-field optimization, RMSD, constrained embedding |
| 9 | Visualization | single and grid images, substructure highlighting, custom drawer options, Jupyter integration, fingerprint bit environments |
| 10 | Molecular modification | explicit hydrogens, Kekulization, aromaticity, substructure replacement, charge neutralization |
| 11 | Hashes and standardization | Murcko scaffold and canonical hashes, regioisomer hashes, randomized SMILES for augmentation |
| 12 | Pharmacophore and 3D features | feature factories and feature extraction |
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
- Forgetting to check for None: Always validate molecules after parsing
- Sanitization failures: Use
DetectChemistryProblems()to debug - Missing hydrogens: Use
AddHs()when calculating properties that depend on hydrogen - 2D vs 3D: Generate appropriate coordinates before visualization or 3D analysis
- SMARTS matching rules: Remember that unspecified properties match anything
- Thread safety with MolSuppliers: Don't share supplier objects across threads
Resources
references/
This skill includes detailed API reference documentation:
api_reference.md- Comprehensive listing of RDKit modules, functions, and classes organized by functionalitydescriptors_reference.md- Complete list of available molecular descriptors with descriptionssmarts_patterns.md- Common SMARTS patterns for functional groups and structural features
Load these references when needing specific API details, parameter information, or pattern examples.
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/
Example scripts for common RDKit workflows:
molecular_properties.py- Calculate comprehensive molecular properties and descriptorssimilarity_search.py- Perform fingerprint-based similarity screeningsubstructure_filter.py- Filter molecules by substructure patterns
These scripts can be executed directly or used as templates for custom workflows.
Frequently asked questions about RDKit Cheminformatics Toolkit
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