Is it possible to generate 3D structures from SMILES?

Absolutely. This skill provides guidance for using the ETKDG method for 3D embedding and applying force fields like MMFF94 to optimize the resulting molecular geometry.

Can this skill handle large chemical datasets?

Yes, the skill includes patterns for using SDMolSupplier and MultithreadedSDMolSupplier, which are optimized for iterating through large SDF and SMILES files without exhausting memory.

What is the difference between RDKit and Datamol?

Datamol is a higher-level wrapper for RDKit designed for standard workflows. This RDKit skill is preferred when you need lower-level control, custom sanitization logic, or advanced algorithmic adjustments.

How does RDKit handle molecular sanitization?

RDKit automatically performs sanitization steps such as valence checking, aromaticity perception, and chirality assignment during parsing, but also allows for manual control over these steps for specialized tasks.

RDKit Cheminformatics

Name: RDKit Cheminformatics
Author: lifangda

bylifangda

•

データサイエンスとML

Enables advanced molecular modeling, chemical property calculation, and structural analysis within Python workflows.

The RDKit Cheminformatics skill provides a powerful suite of tools for handling chemical data, from parsing SMILES and SDF files to generating 3D molecular structures. It allows for high-precision molecular manipulation, including substructure searching using SMARTS patterns, calculation of physicochemical descriptors like LogP and TPSA, and the simulation of chemical reactions. This skill is ideal for researchers and developers in drug discovery and computational chemistry who need fine-grained control over molecular sanitization, fingerprinting, and similarity analysis.

主な機能

01Substructure matching and advanced SMARTS pattern querying

02Molecular fingerprinting and Tanimoto similarity metrics

032D/3D coordinate generation with UFF/MMFF force-field optimization

04Molecular I/O for SMILES, SDF, InChI, and MOL formats

05Comprehensive descriptor calculation including MW, LogP, and TPSA

0616 GitHub stars

ユースケース

01High-throughput screening and drug-likeness filtering using Lipinski's Rules

02Building automated chemical reaction pipelines for virtual synthesis

03Generating 3D conformer ensembles for molecular docking and modeling

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lifangda/claude-plugins rdkit

For use in Claude.ai and ChatGPT

Download Skill