Can I use it to visualize molecules?

Absolutely. It supports generating 2D images of molecules with optional substructure highlighting for easy visual analysis.

What is RDKit used for in Claude Code?

RDKit is used for cheminformatics tasks such as parsing molecular formats, calculating physical properties, and performing chemical substructure searches.

Can this skill help with drug-likeness evaluation?

Yes, it can calculate properties needed for Lipinski's Rule of Five, including molecular weight, LogP, and hydrogen bond counts.

Does it support 3D molecular geometry?

Yes, the skill provides guidance for generating 3D coordinates and optimizing conformers using force fields like UFF and MMFF94.

How does it handle chemical reactions?

It uses Reaction SMARTS to define and apply chemical transformations to reactant molecules, preserving atom mapping and stereochemistry.

RDKit Cheminformatics

Name: RDKit Cheminformatics
Author: overtimepog

byovertimepog

데이터 과학 및 ML

Performs advanced molecular modeling and cheminformatics tasks including descriptor calculation, fingerprinting, and substructure searching.

소개

The RDKit Cheminformatics skill provides a powerful toolkit for developers and scientists working on drug discovery, computational chemistry, and molecular analysis. It enables precise control over molecular structures through SMILES/SDF parsing, automated sanitization, 2D/3D coordinate generation, and chemical reaction modeling. This skill is essential for tasks requiring rigorous chemical data processing and the integration of molecular intelligence within Python-based workflows.

주요 기능

Automated molecular sanitization and chemical property validation
Comprehensive molecular I/O for SMILES, SDF, MOL, and InChI formats
High-throughput calculation of descriptors including LogP, MW, and TPSA
Sophisticated 2D/3D coordinate generation and conformer optimization
Advanced substructure searching and pattern matching using SMARTS
0 GitHub stars

사용 사례

Building virtual screening pipelines for pharmaceutical drug discovery
Automating molecular property analysis and data cleaning for chemical datasets
Generating publication-quality 2D visualizations and 3D molecular models

소개

주요 기능

Automated molecular sanitization and chemical property validation
Comprehensive molecular I/O for SMILES, SDF, MOL, and InChI formats
High-throughput calculation of descriptors including LogP, MW, and TPSA
Sophisticated 2D/3D coordinate generation and conformer optimization
Advanced substructure searching and pattern matching using SMARTS
0 GitHub stars

사용 사례

Building virtual screening pipelines for pharmaceutical drug discovery
Automating molecular property analysis and data cleaning for chemical datasets
Generating publication-quality 2D visualizations and 3D molecular models