datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
npx skills add majiayu000/claude-skill-registry --skill datamol-hxk622-tokendance-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Datamol provides a Pythonic abstraction over RDKit for molecular operations, including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, visualization, and batch processing. It returns native rdkit.Chem.Mol objects for compatibility with RDKit.
How it works
- Basic molecule handling: create molecules from SMILES and convert between formats (SMILES, InChI, InChIKey, SELFIES).
- Standardization and sanitization: sanitize molecules and apply full standardization; can standardize SMILES strings directly.
- File I/O: read/write SDF, SMILES, CSV with SMILES, Excel, and cloud-supported files; provides a universal reader that auto-detects formats.
- Descriptors and properties: compute standard descriptor sets, run batch descriptor computations in parallel, and extract individual descriptors (aromaticity, stereochemistry, flexibility).
- Fingerprints and similarity: generate fingerprints (e.g., ECFP) and compute pairwise distances or similarities; supports distance matrices and clustering by similarity.
- Clustering and diversity: perform Butina clustering and provide diversity-picking utilities and centroid selection.
- Scaffold analysis: extract Bemis-Murcko scaffolds, group by scaffolds, and split data by scaffold for ML.
- Fragmentation: BRICS and RECAP fragmentation with fragment analysis and scoring.
- 3D conformers: generate multiple conformers, cluster by RMSD, obtain conformer positions, and SASA calculations.
- Visualization: render molecule grids, aligned images, highlighted substructures, and conformer views.
- Reactions: apply RDKit-based reactions with a sample applying a reaction SMARTS to a molecule and converting products to SMILES.
- Parallelization: many functions support n_jobs with 1 (sequential), -1 (all cores), or specified core counts; progress bars supported for batch operations.
When to use it
Use Datamol for standard drug-discovery workflows that require SMILES handling, standardization, descriptors, fingerprints, clustering, 3D conformers, fragmentation, and visualization, especially when batch processing and parallelization are beneficial.
What it can touch
- Functions support parallelization via n_jobs for various operations including read_sdf, batch_compute_many_descriptors, cluster_mols, pdist, and conformers.sasa.
- It handles molecules and data structures via native rdkit.Chem.Mol objects and DataFrames from the workflows.
Caveats
- Advanced control or highly customized RDKit parameters are available by using RDKit directly, rather than through the wrapper.
- The skill is licensed under Apache-2.0 and is designed to integrate with Claude Code workflows.
# Datamol Cheminformatics Skill ## Overview Datamol is a Python library that provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. Simplify complex molecular operations with sensible defaults, efficient parallelization, and modern I/O capabilities. All molecular objects are native `rdkit.Chem.Mol` instances, ensuring full compatibility with the RDKit ecosystem. **Key capabilities**: - Molecular format conversion (SMILES, SELFIES, InChI) - Structure standardization and sanitization - Molecular descriptors and fingerprints - 3D conformer generation and analysis - Clustering and diversity selection - Scaffold and fragment analysis - Chemical reaction application - Visualization and alignment - Batch processing with parallelization - Cloud storage support via fsspec ## Installation and Setup Guide users to install datamol: ```bash uv pip install datamol ``` **Import convention**: ```python import datamol as dm ``` ## Core Workflows ### 1. Basic Molecule Handling **Creating molecules from SMILES**: ```python import datamol as dm # Single molecule mol = dm.to_mol("CCO") # Ethanol # From list of SMILES smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"] mol
- Overview
- Installation and Setup
- Core Workflows
- 1. Basic Molecule Handling
- 2. Reading and Writing Molecular Files
- 3. Molecular Descriptors and Properties
- 4. Molecular Fingerprints and Similarity
- 5. Clustering and Diversity Selection
- 6. Scaffold Analysis
- 7. Molecular Fragmentation
- 8. 3D Conformer Generation
- 9. Visualization
- 10. Chemical Reactions
- Parallelization
uv pip install datamol
What does the datamol skill do?
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill datamol-hxk622-tokendance-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.