What is the primary purpose of the Datamol skill?

Datamol acts as a lightweight, Pythonic wrapper around RDKit, making complex cheminformatics tasks like molecular parsing and standardization much simpler to implement with sensible defaults.

How does Datamol handle chemical file I/O?

It provides a universal 'open_df' interface that auto-detects formats like SDF, SMI, CSV, and Parquet, with native support for reading directly from cloud storage buckets.

Is Datamol compatible with existing RDKit code?

Yes, Datamol returns native rdkit.Chem.Mol objects, meaning you can use it alongside any existing RDKit functions without conversion overhead.

Does Datamol support 3D molecular modeling?

Yes, it provides a dedicated module for 3D conformer generation using methods like ETKDGv3, including energy minimization and conformer clustering.

Can this skill help with large-scale data processing?

Absolutely. Datamol includes built-in parallelization for batch operations, such as computing descriptors or fingerprints for thousands of molecules simultaneously.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: Microck

byMicrock

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データサイエンスとML

Simplifies molecular cheminformatics workflows by providing a Pythonic abstraction layer over RDKit for drug discovery and molecular analysis.

概要

Datamol is a high-level Python library designed to streamline cheminformatics tasks, offering a simplified interface and sensible defaults for RDKit. It enables researchers and developers to handle molecular format conversion, structure standardization, 3D conformer generation, and descriptor computation with minimal boilerplate and built-in parallelization. Whether you are building datasets for machine learning or performing complex scaffold analysis, Datamol ensures compatibility with the RDKit ecosystem while significantly improving developer productivity in computational chemistry and molecular modeling projects.

主な機能

Automated structure standardization, sanitization, and metal disconnection
Parallelized batch computation of molecular descriptors and fingerprints
Native support for cloud storage (S3, GCS) and Pandas integration
Pythonic molecular format conversion for SMILES, InChI, and SELFIES
High-quality 3D conformer generation with energy minimization
81 GitHub stars

ユースケース

Automating molecular visualization and scaffold-based SAR analysis reports
Preparing standardized molecular datasets for drug discovery machine learning models
Analyzing structural diversity and clustering large chemical libraries for screening

概要

主な機能

Automated structure standardization, sanitization, and metal disconnection
Parallelized batch computation of molecular descriptors and fingerprints
Native support for cloud storage (S3, GCS) and Pandas integration
Pythonic molecular format conversion for SMILES, InChI, and SELFIES
High-quality 3D conformer generation with energy minimization
81 GitHub stars

ユースケース

Automating molecular visualization and scaffold-based SAR analysis reports
Preparing standardized molecular datasets for drug discovery machine learning models
Analyzing structural diversity and clustering large chemical libraries for screening