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":"1.1","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:

#	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.

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. Scaffold splitting for ML: Ensure proper train/test separation by scaffold

8. Align molecules when visualizing SAR series

Error Handling

# Safe molecule creation
def safe_to_mol(smiles):
    try:
        mol = dm.to_mol(smiles)
        if mol is not None:
            mol = dm.standardize_mol(mol)
        return mol
    except Exception as e:
        print(f"Failed to process {smiles}: {e}")
        return None

# Safe batch processing
valid_mols = []
for smiles in smiles_list:
    mol = safe_to_mol(smiles)
    if mol is not None:
        valid_mols.append(mol)

Integration with Machine Learning

Datamol ships with scipy and scikit-learn as dependencies. Import them as normal PyPI packages — they are not scripts bundled in this skill.

import numpy as np

# Feature generation
X = np.array([dm.to_fp(mol) for mol in mols])

# Or descriptors
desc_df = dm.descriptors.batch_compute_many_descriptors(mols, n_jobs=-1)
X = desc_df.values

# Train model (scikit-learn PyPI package)
from sklearn.ensemble import RandomForestRegressor  # third-party library
model = RandomForestRegressor()
model.fit(X, y_target)

# Predict
predictions = model.predict(X_test)

Troubleshooting

Issue: Molecule parsing fails

• Solution: Use dm.standardize_smiles() first or try dm.fix_mol()

Issue: Memory errors with clustering

• Solution: Use dm.pick_diverse() instead of full clustering for large sets

Issue: Slow conformer generation

• Solution: Reduce n_confs or increase rms_cutoff to generate fewer conformers

Issue: Remote file access fails

• Solution: Install the matching fsspec backend (uv pip install s3fs or gcsfs) and verify only the provider credentials needed for that backend are set (see Remote file support above)

Additional Resources

• Datamol Documentation: https://docs.datamol.io/
• RDKit Documentation: https://www.rdkit.org/docs/
• GitHub Repository: https://github.com/datamol-io/datamol