What is the difference between using RDKit and Datamol?

RDKit offers fine-grained, advanced control over molecular sanitization and specialized algorithms, whereas Datamol is a wrapper designed for simpler interface and standard workflows.

Does this skill support batch processing of large datasets?

Yes, it includes patterns for using SDMolSupplier and MultithreadedSDMolSupplier to handle large SDF and SMILES files efficiently.

Can I use this skill to calculate drug-likeness?

Yes, it provides methods to calculate molecular weight, LogP, and hydrogen bond counts to validate molecules against Lipinski's Rule of Five.

How does RDKit handle 3D structures?

RDKit uses the ETKDG method for 3D coordinate generation and can optimize geometries using Universal Force Field (UFF) or MMFF94.

RDKit Cheminformatics Toolkit

Name: RDKit Cheminformatics Toolkit
Author: jackspace

byjackspace

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Data Science & ML

Performs advanced molecular analysis, descriptor calculation, and chemical informatics using the RDKit library.

About

The RDKit Cheminformatics Toolkit skill empowers developers and researchers to handle complex molecular manipulation tasks with precision. It provides comprehensive guidance for reading and writing chemical formats like SMILES and SDF, calculating essential descriptors such as LogP and TPSA, and performing advanced substructure searches using SMARTS. Whether you are building drug discovery pipelines, conducting computational chemistry research, or generating 3D molecular conformers, this skill offers the implementation patterns and best practices needed to leverage RDKit's powerful C++ backend through its Python API.

Key Features

Molecular I/O and batch processing for SMILES, SDF, and InChI
Substructure searching and pattern matching with SMARTS
Physicochemical descriptor calculation (MW, LogP, TPSA, HBD/HBA)
2D/3D coordinate generation and force field optimization
2 GitHub stars
Molecular fingerprinting and Tanimoto similarity analysis

Use Cases

Automating the creation of publication-quality molecular visualizations and grids
Generating diverse 3D conformer ensembles for virtual screening
Filtering large chemical libraries for drug-likeness based on Lipinski's Rule of Five

About

Key Features

Molecular I/O and batch processing for SMILES, SDF, and InChI
Substructure searching and pattern matching with SMARTS
Physicochemical descriptor calculation (MW, LogP, TPSA, HBD/HBA)
2D/3D coordinate generation and force field optimization
2 GitHub stars
Molecular fingerprinting and Tanimoto similarity analysis

Use Cases

Automating the creation of publication-quality molecular visualizations and grids
Generating diverse 3D conformer ensembles for virtual screening
Filtering large chemical libraries for drug-likeness based on Lipinski's Rule of Five