Does Datamol support parallel processing for large datasets?

Yes, Datamol is built with performance in mind and features built-in support for parallelization in descriptor computation, fingerprinting, and file reading.

Can I use Datamol with cloud storage like AWS S3?

Yes, Datamol integrates with fsspec, allowing you to read and write molecular files directly to S3, Google Cloud Storage, or via HTTP.

How does Datamol handle 3D structures?

It provides a streamlined 'conformers' module to generate, minimize using force fields, cluster by RMSD, and visualize 3D molecular conformations.

What is the relationship between Datamol and RDKit?

Datamol is a high-level Pythonic wrapper for RDKit. It provides a more intuitive API and sensible defaults while remaining fully compatible with the RDKit ecosystem by returning native rdkit.Chem.Mol objects.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: Microck

byMicrock

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데이터 과학 및 ML

Simplifies complex molecular informatics workflows by providing a Pythonic interface for RDKit with sensible defaults and built-in parallelization.

소개

Datamol is a specialized Claude Code Skill designed to streamline molecular science and drug discovery tasks. It wraps the powerful RDKit library into a more accessible, high-level API, allowing developers and scientists to perform molecule standardization, property computation, and 3D conformer generation with minimal code. By abstracting complex RDKit operations into intuitive workflows, it enables efficient handling of large molecular datasets with native support for parallel processing and cloud-based file systems like S3 and GCS.

주요 기능

Automated 3D conformer generation, energy minimization, and analysis
Advanced clustering, diversity selection, and Murcko scaffold analysis
Simplified molecular format conversion and standardization (SMILES, InChI, SELFIES)
81 GitHub stars
Seamless I/O support for SDF, SMILES, CSV, and remote cloud storage
High-performance batch computation of molecular descriptors and fingerprints

사용 사례

Calculating molecular similarity and structural diversity for virtual screening
Cleaning and standardizing large chemical datasets for machine learning models
Generating 3D structures and calculating solvent accessible surface area (SASA) for drug discovery

소개

주요 기능

Automated 3D conformer generation, energy minimization, and analysis
Advanced clustering, diversity selection, and Murcko scaffold analysis
Simplified molecular format conversion and standardization (SMILES, InChI, SELFIES)
81 GitHub stars
Seamless I/O support for SDF, SMILES, CSV, and remote cloud storage
High-performance batch computation of molecular descriptors and fingerprints

사용 사례

Calculating molecular similarity and structural diversity for virtual screening
Cleaning and standardizing large chemical datasets for machine learning models
Generating 3D structures and calculating solvent accessible surface area (SASA) for drug discovery