What is the RDKit skill for Claude Code?

It is a specialized capability that allows Claude to provide domain-specific guidance, code snippets, and best practices for using the RDKit library in Python for chemical data tasks.

Does it support SMARTS substructure searching?

Absolutely. The skill includes guidance on defining query patterns using SMARTS to identify functional groups, rings, and complex molecular substructures.

Can Claude help calculate drug-likeness properties?

Yes, the skill covers the calculation of Lipinski's Rule of Five parameters, including molecular weight, LogP, and hydrogen bond donors/acceptors.

Can this skill help with 3D molecular modeling?

Yes, it provides instructions for 3D coordinate generation, conformer embedding, and geometry optimization using force fields like UFF and MMFF94.

When should I use RDKit over Datamol?

Use RDKit for advanced control, custom sanitization, and specialized algorithms; use Datamol for standard workflows requiring a simpler interface.

RDKit Cheminformatics Toolkit

Name: RDKit Cheminformatics Toolkit
Author: ovachiever

byovachiever

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数据科学与机器学习

Provides comprehensive molecular analysis and manipulation capabilities for cheminformatics and drug discovery workflows.

关于

The RDKit skill equips Claude with advanced domain-specific knowledge for processing chemical information, enabling sophisticated tasks such as molecular structure parsing (SMILES/SDF/InChI), descriptor calculation, and substructure searching with SMARTS patterns. It is designed for researchers and developers working in drug discovery, computational chemistry, and bioinformatics who need to automate complex chemical informatics pipelines with fine-grained control over molecular sanitization, fingerprinting, and 3D coordinate generation.

主要功能

Molecular similarity analysis using Morgan and MACCS fingerprints
5 GitHub stars
Calculation of chemical descriptors including LogP, MW, and TPSA
2D/3D coordinate generation and conformer optimization with force fields
Substructure searching and filtering using advanced SMARTS patterns
Molecular I/O support for SMILES, SDF, MOL, and InChI formats

使用场景

Performing scaffold analysis and R-group decomposition in medicinal chemistry
Generating 3D molecular conformations for docking and simulation studies
Automating high-throughput screening and chemical library filtering

关于

主要功能

Molecular similarity analysis using Morgan and MACCS fingerprints
5 GitHub stars
Calculation of chemical descriptors including LogP, MW, and TPSA
2D/3D coordinate generation and conformer optimization with force fields
Substructure searching and filtering using advanced SMARTS patterns
Molecular I/O support for SMILES, SDF, MOL, and InChI formats

使用场景

Performing scaffold analysis and R-group decomposition in medicinal chemistry
Generating 3D molecular conformations for docking and simulation studies
Automating high-throughput screening and chemical library filtering