Can I use Datamol for large datasets?

Absolutely. Datamol includes built-in parallel processing for descriptors, fingerprints, and clustering to handle thousands of molecules efficiently using all available CPU cores.

What molecular formats can I convert?

You can convert between SMILES, SELFIES, InChI, and InChIKey, as well as handle file formats like SDF, SMI, and Parquet.

Is Datamol compatible with RDKit?

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

Does this skill support cloud storage?

Yes, Datamol supports reading and writing molecular files (SDF, CSV, etc.) directly from S3, GCS, and HTTP via fsspec integration.

Does it support 3D conformation generation?

Yes, it provides advanced utilities for generating, minimizing, and clustering 3D conformers using methods like ETKDGv3 and the UFF force field.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: Activer007

byActiver007

Ciencia de Datos y ML

Simplifies molecular cheminformatics workflows with a Pythonic interface for RDKit, handling molecular standardization, descriptors, and 3D conformers.

Acerca de

Datamol is a lightweight abstraction layer for RDKit, designed to streamline molecular data processing in drug discovery and chemical research. It offers high-level functions for molecule conversion, structure standardization, fingerprint generation, and 3D conformer analysis, all while maintaining native compatibility with RDKit objects. By providing sensible defaults and built-in parallelization, it significantly reduces the boilerplate code required for complex cheminformatics tasks and large-scale dataset manipulation, making it an essential tool for computational chemists and data scientists.

Características Principales

Efficient batch computation of molecular descriptors and fingerprints
Advanced structure standardization and sanitization
0 GitHub stars
Simplified molecular format conversion (SMILES, InChI, SELFIES)
3D conformer generation with energy minimization and clustering
High-performance I/O with support for cloud storage (S3, GCS)

Casos de Uso

Generating 3D molecular conformations for docking or spatial analysis
Analyzing molecular diversity and clustering compounds for drug discovery
Preprocessing large chemical datasets for machine learning models

Acerca de

Características Principales

Efficient batch computation of molecular descriptors and fingerprints
Advanced structure standardization and sanitization
0 GitHub stars
Simplified molecular format conversion (SMILES, InChI, SELFIES)
3D conformer generation with energy minimization and clustering
High-performance I/O with support for cloud storage (S3, GCS)

Casos de Uso

Generating 3D molecular conformations for docking or spatial analysis
Analyzing molecular diversity and clustering compounds for drug discovery
Preprocessing large chemical datasets for machine learning models