Does this skill support 3D molecular modeling?

Absolutely. Datamol includes robust tools for 3D conformer generation using ETKDG, energy minimization with UFF force fields, and SASA calculations.

Why use Datamol instead of raw RDKit?

Datamol reduces boilerplate code, handles edge cases with sensible defaults, and provides a more 'Pythonic' experience, making it faster to develop reproducible cheminformatics workflows.

Can Datamol handle large molecular datasets?

Yes, Datamol is built for performance and features built-in parallelization (n_jobs) for batch processing descriptors, fingerprints, and conformers.

What is the relationship between Datamol and RDKit?

Datamol is a Pythonic wrapper for RDKit. It provides a simplified interface and sensible defaults while remaining fully compatible with RDKit by returning native rdkit.Chem.Mol objects.

How does Datamol handle chemical data I/O?

It offers a universal reader and writer system that supports SMILES, SDF, CSV, Excel, and Parquet, with native support for remote storage like AWS S3 and Google Cloud Storage.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

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

About

Datamol is a specialized library designed to streamline the complexities of chemical data science. It provides a lightweight, modern abstraction layer over RDKit, making tasks like molecular standardization, SMILES parsing, and descriptor calculation more accessible and intuitive. Whether you are generating 3D conformers, performing scaffold analysis, or building large-scale drug discovery pipelines, Datamol offers high-performance batch processing and cloud storage support to accelerate your research. It ensures full compatibility with the RDKit ecosystem by utilizing native molecule objects while removing the boilerplate code typically required for molecular informatics.

Key Features

Advanced 3D conformer generation with energy minimization and SASA analysis
Automated structure standardization and sanitization for clean datasets
1 GitHub stars
Seamless molecular format conversion (SMILES, InChI, SELFIES)
Universal I/O supporting local files and cloud storage like S3 or GCS
High-performance parallelized batch computation for descriptors and fingerprints

Use Cases

Preparing large-scale chemical libraries for machine learning model training
Performing virtual screening via 3D molecular conformation and similarity clustering
Building automated SAR (Structure-Activity Relationship) analysis pipelines

About

Key Features

Advanced 3D conformer generation with energy minimization and SASA analysis
Automated structure standardization and sanitization for clean datasets
1 GitHub stars
Seamless molecular format conversion (SMILES, InChI, SELFIES)
Universal I/O supporting local files and cloud storage like S3 or GCS
High-performance parallelized batch computation for descriptors and fingerprints

Use Cases

Preparing large-scale chemical libraries for machine learning model training
Performing virtual screening via 3D molecular conformation and similarity clustering
Building automated SAR (Structure-Activity Relationship) analysis pipelines