Does Datamol support large datasets?

Yes, Datamol is optimized for performance with built-in support for parallel processing (n_jobs) for batch operations like descriptor computation and clustering.

Is Datamol compatible with RDKit?

Yes, Datamol is a wrapper around RDKit and returns native rdkit.Chem.Mol objects, ensuring full compatibility with the RDKit ecosystem.

How does Datamol handle chemical file formats?

It includes a universal reader that auto-detects formats like SDF, SMILES, CSV, and Parquet, and it can even read/write directly from remote cloud storage like AWS S3 or Google Cloud Storage.

Can I use Datamol for 3D molecular modeling?

Absolutely. Datamol provides advanced tools for 3D conformer generation using ETKDGv3, energy minimization, and solvent-accessible surface area (SASA) calculations.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: Activer007

byActiver007

数据科学与机器学习

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

关于

Datamol is a lightweight abstraction layer over RDKit designed to make molecular operations more accessible and efficient for researchers and developers. It provides a simplified interface for common drug discovery tasks such as structure standardization, descriptor calculation, fingerprinting, and 3D conformer generation. By offering sensible defaults and built-in parallelization, Datamol streamlines the preparation of chemical datasets for machine learning and complex SAR analysis while maintaining full compatibility with native RDKit objects.

主要功能

Parallelized molecular descriptor and fingerprint computation
Integrated I/O support for SDF, SMILES, and cloud storage (S3/GCS)
Efficient 3D conformer generation and energy minimization
Advanced scaffold analysis and Bemis-Murcko decomposition
Molecular structure standardization and sanitization
0 GitHub stars

使用场景

Automating molecular property prediction and drug-likeness filtering
Performing SAR analysis through scaffold-based clustering and grouping
Preparing large-scale molecular datasets for machine learning models

关于

主要功能

Parallelized molecular descriptor and fingerprint computation
Integrated I/O support for SDF, SMILES, and cloud storage (S3/GCS)
Efficient 3D conformer generation and energy minimization
Advanced scaffold analysis and Bemis-Murcko decomposition
Molecular structure standardization and sanitization
0 GitHub stars

使用场景

Automating molecular property prediction and drug-likeness filtering
Performing SAR analysis through scaffold-based clustering and grouping
Preparing large-scale molecular datasets for machine learning models