/datamol

name: datamol
description: 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.
license: Apache-2.0 license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.8+ and datamol (uv pip install). RDKit is installed automatically as a datamol dependency (since 0.12.2). Optional s3fs/gcsfs for cloud I/O via fsspec.
metadata:
  version: "1.2"
  skill-author: K-Dense Inc.

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.

**Version note:** Examples target **datamol 0.12.x** (PyPI stable: **0.12.5**, June 2024). Since 0.10.0, modules are lazy-loaded by default (set `DATAMOL_DISABLE_LAZY_LOADING=1` to disable). Since 0.12.2, RDKit is a direct PyPI dependency of datamol. Fingerprints use RDKit's `rdFingerprintGenerator` API (0.12.5+).

**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:

uv pip install datamol

RDKit is installed automatically with datamol. For remote file paths (S3, GCS, HTTP), install the matching fsspec backend:

uv pip install s3fs   # AWS S3
uv pip install gcsfs  # Google Cloud Storage

**Import convention**:

import datamol as dm

Core Workflows

Ten workflow areas, each with worked code, are documented in [references/core_workflows.md](references/core_workflows.md):

| # | Area | Covers | | --- | --- | --- | | 1 | Basic molecule handling | `to_mol`, batch conversion, error handling, canonical and isomeric SMILES, sanitization and full standardization | | 2 | Reading and writing files | SDF, SMILES, CSV, Excel with rendered structures, the universal reader/writer, and cloud or HTTPS paths | | 3 | Descriptors and properties | the standard descriptor set, parallel computation, aromaticity, stereochemistry, flexibility, and filtering | | 4 | Fingerprints and similarity | ECFP4 and other types, pairwise and cross-set distances, nearest-neighbour lookup (Tanimoto distance = 1 − similarity) | | 5 | Clustering and diversity | similarity clustering, diverse subset picking, and cluster centroids | | 6 | Scaffold analysis | Bemis-Murcko scaffolds, grouping and counting, and scaffold-disjoint train/test splits | | 7 | Fragmentation | fragmenting molecules, finding common fragments across a library, and fragment-based scoring | | 8 | 3D conformers | generation, access, RMSD clustering, representative selection, and SASA | | 9 | Visualization | grids, files, publication SVG, substructure alignment, atom and bond highlighting, conformer display | | 10 | Chemical reactions | reaction SMARTS, applying to a molecule or a whole library |

Three end-to-end pipelines — load/filter/analyze, SAR by scaffold series, and virtual screening — are in [references/workflow_patterns.md](references/workflow_patterns.md).

Parallelization

Datamol includes built-in parallelization for many operations. Use `n_jobs` parameter:

• `n_jobs=1`: Sequential (no parallelization)
• `n_jobs=-1`: Use all available CPU cores
• `n_jobs=4`: Use 4 cores

**Functions supporting parallelization**:

• `dm.read_sdf(..., n_jobs=-1)`
• `dm.descriptors.batch_compute_many_descriptors(..., n_jobs=-1)`
• `dm.cluster_mols(..., n_jobs=-1)`
• `dm.pdist(..., n_jobs=-1)`
• `dm.conformers.sasa(..., n_jobs=-1)`

**Progress bars**: Many batch operations support `progress=True` parameter.

Reference Documentation

For detailed API documentation, consult these reference files:

• **`references/core_api.md`**: Core namespace functions (conversions, standardization, fingerprints, clustering)
• **`references/io_module.md`**: File I/O operations (read/write SDF, CSV, Excel, remote files)
• **`references/conformers_module.md`**: 3D conformer generation, clustering, SASA calculations
• **`references/descriptors_viz.md`**: Molecular descriptors and visualization functions
• **`references/fragments_scaffolds.md`**: Scaffold extraction, BRICS/RECAP fragmentation
• **`references/reactions_data.md`**: Chemical reactions and toy datasets

Best Practices

1. **Always standardize molecules** from external sources:

   mol = dm.standardize_mol(mol, disconnect_metals=True, normalize=True, reionize=True)

2. **Check for None values** after molecule parsing:

   mol = dm.to_mol(smiles)
   if mol is None:
       # Handle invalid SMILES

3. **Use parallel processing** for large datasets:

   result = dm.operation(..., n_jobs=-1, progress=True)

4. **Use cloud I/O only when requested** — confirm remote write paths; install `s3fs`/`gcsfs` as needed:

   df = dm.read_sdf("s3://bucket/compounds.sdf")

5. **Use appropriate fingerprints** for similarity:

• ECFP (Morgan): General purpose, structural similarity
• MACCS: Fast, smaller feature space
• Atom pairs: Considers atom pairs and distances

6. **Consider scale limitations**:

• Butina clustering: ~1,000 molecules (full distance matrix)
• For larger datasets: Use diversity selection or hierarchical methods

7. **Scaff