How does this differ from using the datamol library?

While datamol is a simplified wrapper around RDKit, this skill focuses on RDKit directly for advanced control, custom sanitization, and access to specialized algorithms.

What is the RDKit Claude Code skill used for?

It is used to perform advanced molecular analysis, including parsing chemical formats, calculating physical descriptors, and generating 3D structures for drug discovery and computational chemistry.

Can I use this for substructure searching?

Absolutely, it includes comprehensive guidance on using SMARTS patterns for fine-grained substructure matching and molecular filtering.

Can it help with molecular visualization?

Yes, it provides code patterns for drawing molecules, generating grids of images, and highlighting specific substructures for visual analysis.

Does this skill support SMILES and SDF formats?

Yes, it provides detailed implementation patterns for reading and writing molecules in SMILES, SDF, InChI, and MOL formats, including batch processing.

RDKit Cheminformatics

Name: RDKit Cheminformatics
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Provides comprehensive cheminformatics capabilities for molecular analysis, structural manipulation, and computational chemistry workflows.

About

RDKit is a powerful open-source library for cheminformatics that enables precise control over molecular data. This skill equips Claude with the expertise to parse SMILES and SDF formats, calculate physical properties like LogP and TPSA, perform substructure searches using SMARTS, and generate 3D molecular conformations. It is an essential resource for researchers and developers working in drug discovery, computational chemistry, and materials science who need to automate complex chemical informatics tasks directly within their coding environment.

Key Features

Molecular fingerprinting and Tanimoto similarity analysis
Molecular I/O for SMILES, SDF, InChI, and MOL formats
2D/3D coordinate generation and force-field conformer optimization
Advanced substructure searching and matching with SMARTS patterns
3 GitHub stars
Property calculation including MW, LogP, TPSA, and H-bond capacity

Use Cases

Automating molecular descriptor calculation for high-throughput chemical datasets
Designing virtual screening pipelines for drug discovery and lead optimization
Visualizing and highlighting specific functional groups or structural motifs

About

Key Features

Molecular fingerprinting and Tanimoto similarity analysis
Molecular I/O for SMILES, SDF, InChI, and MOL formats
2D/3D coordinate generation and force-field conformer optimization
Advanced substructure searching and matching with SMARTS patterns
3 GitHub stars
Property calculation including MW, LogP, TPSA, and H-bond capacity

Use Cases

Automating molecular descriptor calculation for high-throughput chemical datasets
Designing virtual screening pipelines for drug discovery and lead optimization
Visualizing and highlighting specific functional groups or structural motifs