Datamol is a Python library that acts as a Pythonic wrapper around RDKit, making molecular cheminformatics tasks easier, more readable, and faster to implement.

Can I use Datamol for large-scale data processing?

Absolutely. Datamol includes built-in parallel processing features to handle batch computations for descriptors, fingerprints, and molecular cleaning efficiently.

Is Datamol suitable for 3D molecular modeling?

Yes, it provides a dedicated conformers module for generating 3D structures, performing energy minimization, and clustering conformers by RMSD.

Does Datamol support cloud storage for chemical files?

Yes, it integrates with fsspec to allow reading and writing molecular files (SDF, CSV, etc.) directly to S3, Google Cloud Storage, or via HTTP.

Does this skill require RDKit to be installed?

Yes, Datamol is built on top of RDKit and returns native RDKit objects, ensuring full compatibility with the RDKit ecosystem.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: Activer007

byActiver007

Data Science & ML

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

About

Datamol is a lightweight abstraction layer for RDKit designed to streamline molecular data processing for researchers and developers. It provides sensible defaults for complex operations such as molecular standardization, fingerprint generation, 3D conformer modeling, and scaffold analysis. By automating boilerplate code and enabling seamless parallel processing and cloud I/O support, Datamol allows users to focus on chemical insights and drug discovery rather than low-level library management.

Key Features

Efficient batch processing with built-in multi-core parallelization
0 GitHub stars
Standardized molecular format conversion for SMILES, InChI, and SELFIES
Advanced 3D conformer generation, energy minimization, and clustering
Automated molecular standardization and sanitization for clean datasets
High-level scaffold and fragment analysis including Murcko and BRICS

Use Cases

Automating drug-likeness filtering and multi-parameter optimization (MPO)
Preparing large-scale molecular datasets for chemical machine learning models
Analyzing structural diversity and clustering compound libraries for screening

About

Key Features

Efficient batch processing with built-in multi-core parallelization
0 GitHub stars
Standardized molecular format conversion for SMILES, InChI, and SELFIES
Advanced 3D conformer generation, energy minimization, and clustering
Automated molecular standardization and sanitization for clean datasets
High-level scaffold and fragment analysis including Murcko and BRICS

Use Cases

Automating drug-likeness filtering and multi-parameter optimization (MPO)
Preparing large-scale molecular datasets for chemical machine learning models
Analyzing structural diversity and clustering compound libraries for screening