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.

Verified target: datamol 0.13.0, released September 9, 2026. Local checks used Python 3.13 and RDKit 2026.03.6. Python 3.11+ and RDKit 2024.09+ are required. The 0.13 distribution includes SELFIES, visualization, cloud, Excel and Parquet runtime dependencies. Current API examples and verification limits are recorded in references/review.md. Pin the environment: RDKit upgrades can change canonical representations and retained conformers. Compare chemical invariants, not a fixed conformer count or an exact version-dependent canonical string.

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==0.13.0"

RDKit and the S3/GCS, Excel/Parquet, visualization and SELFIES dependencies are installed by the current distribution; separate feature extras are not required. Use an isolated environment to avoid conflicting scientific-package requirements.

Import convention:

import datamol as dm

Core Workflows

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

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, batch_size=128)
• 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. For parallel descriptor batches, supply a positive batch_size (for example 128). In the tested 0.13.0 stack the default None reaches joblib and raises when parallelism is enabled.

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. Choose and record a task-specific standardization policy for external molecules. Preserve original structures and IDs alongside transformed ones; metal disconnection, neutralization, salt stripping, and stereochemistry changes can alter the assayed entity. Do not apply these transformations automatically to organometallic or formulation tasks. The following is an illustrative policy for inputs where metal disconnection is intended: ``python mol = dm.standardize_mol(mol, disconnect_metals=True, normalize=True, reionize=True)

1. Check for None values after molecule parsing: ``python mol = dm.to_mol(smiles) if mol is None: raise ValueError("Invalid SMILES; retain the source row in the rejection log")

1. Use parallel processing for large datasets: ``python result = dm.parallelized(dm.to_mol, smiles_list, n_jobs=-1, progress=True)

1. Use cloud I/O for the requested remote paths with the selected provider credentials: ``python df = dm.read_sdf("s3://bucket/compounds.sdf", as_df=True, mol_column="mol")

1. Use appropriate fingerprints for similarity:
2. ECFP (Morgan): General purpose, structural similarity
3. MACCS: Fast, smaller feature space
4. Atom pairs: Considers atom pairs and distances

1. Consider scale limitations:
2. Butina clustering stores O(N²) pairwise distances; choose a size limit from the memory budget.
3. For larger datasets: use pick_diverse with a bounded npick; hierarchical clustering can also need quadratic memory.

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

1. Align molecules when visualizing SAR series

Error Handling

Illustrative input policy: provide smiles_list and retain source IDs plus failures alongside the accepted molecules. Standardization is task-specific, not a repair guarantee.

# 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

Illustrative model template: supply aligned train_mols, y_target, and held-out test_mols. Split compounds by the task-appropriate scaffold/group before fitting any learned preprocessing.

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

# Use the same fingerprint schema for training and held-out molecules.
fp_options = dict(fp_type="ecfp", radius=2, fpSize=2048, includeChirality=True)
X = np.stack([dm.to_fp(mol, **fp_options) for mol in train_mols])
X_test = np.stack([dm.to_fp(mol, **fp_options) for mol in test_mols])

# Train model (scikit-learn PyPI package)
from sklearn.ensemble import RandomForestRegressor  # third-party library
model = RandomForestRegressor(random_state=42)
model.fit(X, y_target)

# Predict
predictions = model.predict(X_test)

Troubleshooting

Issue: Molecule parsing fails

• Solution: Retain the failed source record and diagnose syntax/valence first. Standardization is not guaranteed to repair invalid chemistry; inspect any fix_mol() result and record the transformation before treating it as the original compound.

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 to lower embedding work. RMS pruning happens after embedding/minimization, so a larger rms_cutoff reduces retained conformers without avoiding the initial work

Issue: Remote file access fails

• Solution: Verify the selected fsspec protocol and provider credentials. S3/GCS dependencies are included in 0.13.0; other backends may need installation. Remote authorization and writes were not tested here

Additional Resources

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