Does it support cloud storage?

Yes, Datamol leverages fsspec to support reading and writing molecular files directly from S3, GCS, and HTTP locations.

How do I install the required dependencies?

You can install Datamol via conda/mamba (recommended) or pip. The skill provides the specific commands needed to set up the environment.

Can Datamol handle large datasets?

Absolutely. Datamol includes built-in parallelization and batch processing functions (using n_jobs=-1) to process thousands of molecules efficiently using all CPU cores.

Is Datamol compatible with RDKit?

Yes, Datamol returns native RDKit molecular objects, ensuring full compatibility with the existing RDKit ecosystem while providing a simpler interface.

What is the primary advantage over raw RDKit?

Datamol provides a more intuitive, Pythonic API with sensible defaults, significantly reducing boilerplate code for common tasks like standardization, I/O, and visualization.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: lifangda

bylifangda

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Data Science & ML

Simplifies molecular cheminformatics workflows by providing a Pythonic interface for RDKit with sensible defaults and parallel processing.

About

Datamol is a lightweight abstraction layer designed to streamline drug discovery and chemical data analysis. It enhances the RDKit ecosystem by providing easier SMILES parsing, automated molecule standardization, efficient batch descriptor computation, and 3D conformer generation. By offering a more intuitive API and native parallelization, it allows researchers and developers to handle complex molecular operations with fewer lines of code while maintaining full compatibility with RDKit objects.

Key Features

High-performance batch computation of descriptors and fingerprints
Automated molecular structure standardization and sanitization
Built-in parallel processing and cloud storage support
Pythonic SMILES, SELFIES, and InChI conversion
Advanced 3D conformer generation and analysis
16 GitHub stars

Use Cases

Generating and optimizing 3D conformations for molecular docking
Analyzing molecular diversity and clustering for library design
Preprocessing large chemical datasets for machine learning models

About

Key Features

High-performance batch computation of descriptors and fingerprints
Automated molecular structure standardization and sanitization
Built-in parallel processing and cloud storage support
Pythonic SMILES, SELFIES, and InChI conversion
Advanced 3D conformer generation and analysis
16 GitHub stars

Use Cases

Generating and optimizing 3D conformations for molecular docking
Analyzing molecular diversity and clustering for library design
Preprocessing large chemical datasets for machine learning models