Standardize and featurize molecular structures from SMILES and files for cheminformatics.
npx skills add https://github.com/shushuzn/Rairos --skill datamol-shushuzn
Please help me install this Agent Skill. Skill: datamol Source: https://github.com/shushuzn/Rairos/tree/main/skills/datamol Command: npx skills add https://github.com/shushuzn/Rairos --skill datamol-shushuzn
Datamol removes friction from everyday cheminformatics by giving you a Pythonic, RDKit-compatible workflow for turning strings into validated molecules, computing chemistry features, and running common analysis steps without fighting low-level RDKit APIs.
Ask the AI to convert your SMILES strings into standardized RDKit molecules, compute descriptors in parallel, and output a table of results for further filtering and modeling.
High-intent search queries and answers about installing and using this skill.
You can convert SMILES strings into RDKit molecules by parsing and sanitizing them to apply consistent standardization, ensuring downstream descriptors and fingerprints behave reliably. This Skill returns native rdkit.Chem.Mol objects ready for analysis.
Yes, you can compute drug-likeness descriptors and similarity-ready fingerprints in parallel using the n_jobs parameter. This supports batch workflows for screening, ranking, and dataset building at scale.
Generating 3D conformers from SMILES involves embedding 3D structures, minimizing energies, and clustering conformers. You can also compute SASA values for conformational studies and visualize conformers for inspection.
Yes, the workflow supports reading and writing SDF, SMI, CSV, and Excel formats, including optional remote paths via fsspec. This allows smooth file handling and integration into batch molecule conversion operations.
You can cluster molecules by similarity and select diverse subsets as part of batch cheminformatics workflows. This process utilizes computed fingerprints to run scaling analyses for SAR-style workflows and scaffold fragmentation.
Molecular standardization is required before computing fingerprints because it applies consistent sanitization to parsed structures. Without it, downstream descriptors and fingerprints may behave unreliably during screening and ranking.
ovachieverConvert SMILES or RDKit molecules into ML-ready numerical representations.
jinzhezenggroupGenerate RDKit conformers from SMILES and output SDF or XYZ files.
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yf8578Provide a Pythonic interface for RDKit cheminformatics tasks.
