Wrap RDKit with a Pythonic API for molecular conversion, standardization, and descriptors.
npx skills add https://github.com/OwnLabAI/ownlab --skill datamol-ownlabai
Please help me install this Agent Skill. Skill: datamol Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/datamol Command: npx skills add https://github.com/OwnLabAI/ownlab --skill datamol-ownlabai
Datamol provides a Pythonic wrapper around RDKit to simplify molecular cheminformatics tasks, including format conversion, standardization, descriptors, fingerprints, clustering, and 3D conformer workflows, enabling scalable data pipelines.
Install datamol and load a molecule with a simple example to begin.
High-intent search queries and answers about installing and using this skill.
RDKit cheminformatics workflows are simplified by wrapping core functions in a Pythonic API for molecule conversion, standardization, descriptor calculation, and fingerprint generation. This approach reduces boilerplate code for routine molecular data processing tasks.
Batch processing molecular datasets from SMILES and SDF formats is supported through built-in parallelization utilities. You can scale analyses across datasets of varying sizes and export results to CSV, SDF, or Parquet using fsspec for seamless I/O.
3D conformer generation and SAR visualization are supported for structure-activity relationship analysis. The Skill provides tools to generate conformers, cluster molecular data, and visualize structure-activity relationships directly within your cheminformatics pipelines.
Standardizing molecular structures and calculating drug-likeness is handled through robust format conversion and descriptor filtering functions. Applying these workflows enables efficient dataset screening by evaluating molecular properties and filtering compounds based on drug-likeness criteria.
RDKit is required to run datamol for molecular format conversion, standardization, and descriptor calculation. Optional I/O and parallelization utilities are also available to scale analyses and handle batch processing across large chemical datasets.
Fsspec is utilized to enable seamless data I/O across multiple file formats like CSV, SDF, and Parquet. It allows the molecular data processing pipelines to handle diverse storage backends and scale batch operations efficiently across varying dataset sizes.
ovachieverConvert SMILES or RDKit molecules into ML-ready numerical representations.
jasrajtulsiParse and analyze molecular structures with RDKit for cheminformatics workflows.
jasrajtulsiParse and standardize molecular structures for cheminformatics workflows.
biomaps-infraParse, standardize, and compute descriptors for drug discovery molecules.
