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-aiskillstore-marketplace-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 offers a Pythonic layer over RDKit for molecular tasks such as SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing. It returns native rdkit.Chem.Mol objects and supports batch operations and cloud storage access via fsspec.
How it works
- Basic molecule handling: create molecules from SMILES (dm.to_mol), convert to SMILES (dm.to_smiles), and handle errors by returning None.
- Standardization and sanitization: sanitize_mol and standardize_mol with options; standardize_smiles for SMILES strings.
- I/O: read and write SDF/SMI/CSV/Excel/open_df with functions like read_sdf, read_smi, read_csv, read_excel, and to_sdf, to_smi, to_xlsx; supports cloud URIs via S3/GCS/HTTP forms.
- Descriptors and properties: compute descriptors (descriptors.compute_many_descriptors), batch computation (descriptors.batch_compute_many_descriptors) with n_jobs and progress; access specific descriptors like n_aromatic_atoms, n_stereo_centers, etc.
- Fingerprints and similarity: to_fp with types like 'ecfp', and distance metrics via pdist and cdist; supports similarity-based ranking.
- Clustering and diversity: cluster_mols (Butina clustering) with cutoff and n_jobs; diversity and centroid selection via pick_diverse and pick_centroids.
- Scaffold analysis and ML splits: to_scaffold_murcko, scaffold-based grouping and train/test splitting by scaffolds.
- Fragmentation: fragment.brics and fragment.recap; fragment analysis with fragment_counts and fragment_score.
- 3D conformers: conformers.generate with n_confs, rms_cutoff, minimize_energy, method; access conformer coordinates; clustering and centroid retrieval.
- SASA and visualization: conformers.sasa for solvent-accessible surface area; visualization helpers via viz.to_image and related conformer visualization.
- Reactions: apply_reaction via rdChemReactions to perform chemical transformations; batch application over libraries.
- Parallelization: n_jobs controls across many functions; progress bars supported.
When to use it
- When you need an approachable, high-level interface for cheminformatics tasks with RDKit integration and parallelization.
- When working with standard drug-discovery workflows including SMILES handling, standardization, descriptors, fingerprints, clustering, 3D conformers, and cloud storage support.
What it can touch
- Core library functions through import alias dm (Datamol). Uses functions like dm.to_mol, dm.read_sdf, dm.descriptors.compute_many_descriptors, dm.cluster_mols, dm.to_fp, dm.pdist, dm.reactions.apply_reaction, dm.conformers.generate, dm.viz.to_image, etc.
- Parallel execution via n_jobs parameter (e.g., n_jobs=-1).
Caveats
- License: Apache-2.0 license; usage should comply with license terms.
- Returns native rdkit.Chem.Mol objects, which requires RDKit to be installed in the environment.
- Some workflows reference external documentation files for deeper details (e.g., references/core_api.md, references/io_module.md, references/conformers_module.md, references/descriptors_viz.md).
# 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-aiskillstore-marketplace-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.