datamol
Category: chemistry-and-drug-discovery · License: Apache-2.0 license · Version: 1.2
Tags: cheminformatics, RDKit, molecules, SMILES, drug discovery
When to use it
Wraps RDKit through the datamol Python library (import datamol as dm) for molecular cheminformatics, returning native rdkit.Chem.Mol objects. Covers SMILES/SELFIES/InChI conversion, sanitization and standardization, descriptors, fingerprints and similarity, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds, BRICS/RECAP fragmentation, 3D conformers, reactions, SDF/CSV/Excel I/O including cloud paths, and parallel batch processing. Use when parsing or standardizing SMILES from external sources, computing descriptors or fingerprints for a compound library, clustering or selecting diverse molecules, making scaffold-based train/test splits, or generating conformers. Use when running a molecule-processing pipeline with n_jobs. For fine-grained control or custom parameters, use RDKit directly instead.Example requests
Ask your agent in plain language, for example:- “How do I get a canonical, standardized SMILES out of a messy list of vendor SMILES using datamol?”
- “I have vendor_library.sdf with about 40k compounds and I need to split it into train/test for a QSAR model without scaffold leakage. Then I want 2048-bit ECFP fingerprints for each compound. It’s slow, so please use all cores. Can you write the script?”
- “I’ve got 5,000 hit compounds from a screen and can only afford to order 96. I want those 96 to cover as much chemical space as possible and not be near-duplicates of each other. How should I pick them?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole chemistry-and-drug-discovery category with /plugin install chemistry-and-drug-discovery@research-agent-skills.