Can I use Datamol for 3D molecular modeling?

Yes, Datamol includes robust tools for generating 3D conformers, minimizing energy using force fields, and calculating Solvent Accessible Surface Area (SASA).

What is the primary benefit of using Datamol over raw RDKit?

Datamol provides a more Pythonic interface with simplified functions, sensible defaults, and built-in parallelization for batch operations, reducing the amount of boilerplate code needed.

Does Datamol support cloud storage?

Yes, it supports reading from and writing to remote storage like S3, GCS, and HTTP via fsspec integration.

Is Datamol compatible with RDKit objects?

Absolutely. Datamol returns native rdkit.Chem.Mol objects, ensuring full compatibility with the existing RDKit ecosystem and other chemistry libraries.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: ricable

byricable

•

Ciencia de Datos y ML

Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.

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Datamol provides a lightweight, high-level abstraction layer over RDKit, making complex molecular operations accessible through sensible defaults and modern Python patterns. It enables researchers and developers to handle SMILES parsing, structure standardization, 3D conformer generation, and parallelized molecular descriptor computation with minimal boilerplate. By returning native RDKit objects, it ensures seamless integration with the broader scientific computing ecosystem while significantly accelerating common tasks in drug discovery and chemical data analysis.

Características Principales

Molecular format conversion for SMILES, InChI, and SELFIES
Advanced scaffold analysis and molecular fragmentation
3D conformer generation with UFF force field minimization
Batch processing with built-in parallelization and progress tracking
Structure standardization and sanitization for high-quality datasets
1 GitHub stars

Casos de Uso

Identifying lead compounds through structural clustering and scaffold analysis
Generating 3D molecular structures for docking and simulation workflows
Preparing and cleaning large chemical datasets for machine learning models

Acerca de

Características Principales

Molecular format conversion for SMILES, InChI, and SELFIES
Advanced scaffold analysis and molecular fragmentation
3D conformer generation with UFF force field minimization
Batch processing with built-in parallelization and progress tracking
Structure standardization and sanitization for high-quality datasets
1 GitHub stars

Casos de Uso

Identifying lead compounds through structural clustering and scaffold analysis
Generating 3D molecular structures for docking and simulation workflows
Preparing and cleaning large chemical datasets for machine learning models