Does this skill support cloud storage?

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

Is it possible to visualize molecules in Claude Code using this?

While the skill generates visualization code and files (PNG/SVG), actual image rendering depends on your terminal environment's ability to display images or saved files.

Can I process large chemical libraries with this skill?

Yes, Datamol features built-in parallel processing for batch operations like descriptor computation and clustering, making it suitable for large datasets.

What 3D conformer generation methods are supported?

It primarily uses the ETKDGv3 embedding method and provides tools for UFF energy minimization and RMSD-based conformer clustering.

What is the relationship between Datamol and RDKit?

Datamol is a Pythonic wrapper around RDKit. it simplifies the RDKit API with sensible defaults while returning native rdkit.Chem.Mol objects for full compatibility.

Datamol Cheminformatics

Name: Datamol Cheminformatics
Author: jimmc414

byjimmc414

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324

Data Science & ML

Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic abstraction layer over RDKit.

About

Datamol is a specialized scientific skill designed to streamline molecular data processing within Claude Code. It provides a lightweight, intuitive interface for complex RDKit operations, including SMILES parsing, structure standardization, and property computation. By offering sensible defaults and built-in parallelization, it allows users to perform advanced tasks like 3D conformer generation, scaffold analysis, and molecular visualization with minimal boilerplate. It is an essential tool for researchers and developers working on autonomous discovery, chemical informatics, and drug design pipelines.

Key Features

High-performance batch computation of molecular descriptors and fingerprints
Comprehensive visualization tools for molecular grids and scaffold alignments
Automated structure standardization and sanitization for chemical datasets
324 GitHub stars
Seamless conversion between SMILES, SELFIES, InChI, and SDF formats
Advanced 3D conformer generation with energy minimization and RMSD clustering

Use Cases

Generating 3D molecular conformations for structural biology and docking simulations
Automating scaffold-based analysis and diversity selection for compound libraries
Preparing and cleaning molecular datasets for machine learning models

About

Key Features

High-performance batch computation of molecular descriptors and fingerprints
Comprehensive visualization tools for molecular grids and scaffold alignments
Automated structure standardization and sanitization for chemical datasets
324 GitHub stars
Seamless conversion between SMILES, SELFIES, InChI, and SDF formats
Advanced 3D conformer generation with energy minimization and RMSD clustering

Use Cases

Generating 3D molecular conformations for structural biology and docking simulations
Automating scaffold-based analysis and diversity selection for compound libraries
Preparing and cleaning molecular datasets for machine learning models