Is Datamol compatible with RDKit?

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

Can I process large chemical datasets in parallel?

Yes, Datamol features built-in parallelization support (using n_jobs) for batch processing of molecules, descriptors, and fingerprints to maximize CPU efficiency.

Does it support cloud storage for molecular files?

Yes, through fsspec integration, Datamol can read and write molecular files directly from S3, Google Cloud Storage, and via HTTP, facilitating cloud-native research.

What is Datamol used for in Claude Code?

It provides Claude with specialized capabilities to handle molecular data, perform chemical structure standardization, and compute molecular descriptors using a simplified Pythonic interface.

What file formats does Datamol support for I/O?

It supports common chemistry and data formats including SMILES (.smi), SDF, CSV, Excel, Parquet, and JSON.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: x-cmd

byx-cmd

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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 provides a lightweight, high-level abstraction layer over RDKit, making complex molecular operations accessible and efficient for AI-assisted coding. It streamlines essential tasks such as SMILES parsing, structure standardization, 3D conformer generation, and batch descriptor computation with built-in parallelization and cloud storage support. By returning native RDKit molecular objects, Datamol ensures full compatibility with the broader cheminformatics ecosystem while offering sensible defaults for standard drug discovery pipelines and chemical data analysis.

主要功能

Efficient batch computation of molecular descriptors and fingerprints
Built-in parallelization for high-throughput chemical data processing
Advanced 3D conformer generation and RMSD clustering
Simplified molecular format conversion and structure standardization
8 GitHub stars
Native cloud storage support for S3, GCS, and HTTP molecular files

使用场景

Visualizing molecular scaffolds and analyzing structural diversity in chemical datasets
Standardizing and cleaning large chemical libraries for machine learning models
Building high-throughput screening pipelines for drug discovery

关于

主要功能

Efficient batch computation of molecular descriptors and fingerprints
Built-in parallelization for high-throughput chemical data processing
Advanced 3D conformer generation and RMSD clustering
Simplified molecular format conversion and structure standardization
8 GitHub stars
Native cloud storage support for S3, GCS, and HTTP molecular files

使用场景

Visualizing molecular scaffolds and analyzing structural diversity in chemical datasets
Standardizing and cleaning large chemical libraries for machine learning models
Building high-throughput screening pipelines for drug discovery