How do I install the required library for this skill?

You can install the library directly in your environment using 'uv pip install datamol' or 'pip install datamol' to enable these cheminformatics capabilities.

Does Datamol work with standard RDKit objects?

Yes, Datamol functions return native rdkit.Chem.Mol objects, ensuring 100% compatibility with the existing RDKit ecosystem and advanced RDKit functions.

Does this skill support 3D molecular structures?

Yes, it provides robust capabilities for 3D conformer generation using the ETKDGv3 method, energy minimization, and geometric analysis like RMSD clustering.

What is the main advantage of using Datamol over raw RDKit?

Datamol provides a more Pythonic, simplified interface with sensible defaults, built-in parallelization, and modern file I/O that significantly reduces the boilerplate code required for common chemistry tasks.

Can I use Datamol for large-scale molecular datasets?

Absolutely; Datamol is built for performance and includes batch processing functions that use parallelization to handle libraries with thousands of molecules efficiently.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: Activer007

byActiver007

データサイエンスとML

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

概要

Datamol is a lightweight abstraction layer for RDKit designed to streamline standard drug discovery tasks and molecular data manipulation. It enables researchers and developers to handle molecular format conversions, perform rigorous structure standardization, compute chemical descriptors, and generate 3D conformers with minimal boilerplate code. By returning native RDKit objects and supporting modern I/O for cloud storage, Datamol bridges the gap between high-level ease of use and low-level chemical informatics control, making it an essential tool for AI-driven molecular modeling and large-scale dataset curation within Claude Code.

主な機能

Molecular clustering, diversity selection, and scaffold analysis
High-performance batch descriptor and fingerprint computation
0 GitHub stars
3D conformer generation, energy minimization, and SASA calculation
Built-in parallelization and native cloud storage support (S3, GCS)
Molecular format conversion and standardization (SMILES, SELFIES, InChI)

ユースケース

Identifying diverse chemical subsets and analyzing structural scaffolds
Generating 3D molecular conformations for computational modeling
Automating drug discovery pipelines and molecular dataset curation

概要

主な機能

Molecular clustering, diversity selection, and scaffold analysis
High-performance batch descriptor and fingerprint computation
0 GitHub stars
3D conformer generation, energy minimization, and SASA calculation
Built-in parallelization and native cloud storage support (S3, GCS)
Molecular format conversion and standardization (SMILES, SELFIES, InChI)

ユースケース

Identifying diverse chemical subsets and analyzing structural scaffolds
Generating 3D molecular conformations for computational modeling
Automating drug discovery pipelines and molecular dataset curation