Alterlab Datamol

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:

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uv pip install datamol

Examples here are verified against datamol 0.12.x (pulls in RDKit automatically). The descriptor key names below are stable in this line; pin if you depend on them: uv pip install 'datamol>=0.12,<0.13'.

Import convention:

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import datamol as dm

Core Workflows

Each subsection below shows the primary call pattern. Full API signatures, parameters, and secondary examples live in the per-module reference files cited under each; complete multi-step pipelines live in references/workflow_recipes.md.

1. Basic Molecule Handling

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import datamol as dm

# Parse SMILES (returns None on failure)
mol = dm.to_mol("CCO")                        # Ethanol
mols = [dm.to_mol(smi) for smi in ["CCO", "c1ccccc1", "CC(=O)O"]]
if dm.to_mol("invalid_smiles") is None:
    print("Failed to parse SMILES")

# Export to common formats (canonical + isomeric by default)
smiles   = dm.to_smiles(mol)                  # keeps stereochemistry
flat     = dm.to_smiles(mol, isomeric=False)  # drops stereochemistry
inchi    = dm.to_inchi(mol)
inchikey = dm.to_inchikey(mol)
selfies  = dm.to_selfies(mol)

# Standardize user-provided molecules (recommended for datasets)
mol = dm.sanitize_mol(mol)
mol = dm.standardize_mol(mol, disconnect_metals=True, normalize=True, reionize=True)
clean_smiles = dm.standardize_smiles(smiles)

Full conversion, sanitization, and standardization API: see references/core_api.md.

2. Reading and Writing Molecular Files

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# Read (open_df auto-detects .sdf/.csv/.xlsx/.parquet/.json)
df = dm.read_sdf("compounds.sdf", mol_column='mol')
df = dm.read_csv("data.csv", smiles_column="SMILES", mol_column="mol")
df = dm.open_df("file.sdf")

# Write
dm.to_sdf(mols, "output.sdf")               # or dm.to_sdf(df, "output.sdf", mol_column="mol")
dm.to_smi(mols, "output.smi")
dm.to_xlsx(df, "output.xlsx", mol_columns=["mol"])   # renders molecule images in cells

# Remote paths work everywhere via fsspec (S3, GCS, HTTP)
df = dm.read_sdf("s3://bucket/compounds.sdf")
dm.to_sdf(mols, "s3://bucket/output.sdf")

Full reader/writer signatures (read_smi, read_excel, read_mol2file, read_pdbfile, save_df, shared parameters): see references/io_module.md.

3. Molecular Descriptors and Properties

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# Single molecule -> ~22 keys. Note datamol's naming (NOT rdkit's):
desc = dm.descriptors.compute_many_descriptors(mol)
#   {'mw': 46.04, 'clogp': -0.0, 'n_lipinski_hbd': 1, 'n_lipinski_hba': 1,
#    'tpsa': 20.23, 'n_rotatable_bonds': 0, 'qed': ..., 'fsp3': ..., 'sas': ..., ...}
# Gotcha: logP is 'clogp'; donors/acceptors are 'n_lipinski_hbd'/'n_lipinski_hba'.
# There is no 'logp', 'hbd', 'hba', or 'n_aromatic_atoms' key in this dict.

# Batch (parallel) -> DataFrame with the same keys
desc_df = dm.descriptors.batch_compute_many_descriptors(mols, n_jobs=-1, progress=True)

# Standalone descriptors not in the dict above
dm.descriptors.n_aromatic_atoms(mol)
dm.descriptors.n_stereo_centers(mol)
dm.descriptors.n_rigid_bonds(mol)

# Drug-likeness filter (Lipinski's Rule of Five) with datamol's exact key names
def is_druglike(mol):
    d = dm.descriptors.compute_many_descriptors(mol)
    return (d['mw'] <= 500 and d['clogp'] <= 5 and
            d['n_lipinski_hbd'] <= 5 and d['n_lipinski_hba'] <= 10)

druglike_mols = [m for m in mols if is_druglike(m)]

Full descriptor catalog, RDKit descriptor access, and ADME examples: see references/descriptors_viz.md.

4. Molecular Fingerprints and Similarity

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# Fingerprints (ECFP/Morgan is the default)
fp       = dm.to_fp(mol, fp_type='ecfp', radius=2, n_bits=2048)
fp_maccs = dm.to_fp(mol, fp_type='maccs')
# Also available: 'topological', 'atompair', 'fcfp'

# Similarity as Tanimoto distance (distance = 1 - similarity; lower = more similar)
distance_matrix = dm.pdist(mols, n_jobs=-1)                       # within one set
distances       = dm.cdist(query_mols, library_mols, n_jobs=-1)  # between two sets
from scipy.spatial.distance import squareform
dist_matrix = squareform(dm.pdist(mols))                         # square form

Fingerprint types and pdist / cdist details: see references/core_api.md.

5. Clustering and Diversity Selection

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# Butina clustering (cutoff = Tanimoto distance; each cluster is a list of indices)
clusters = dm.cluster_mols(mols, cutoff=0.2, n_jobs=-1)
for i, cluster in enumerate(clusters):
    cluster_mols = [mols[idx] for idx in cluster]

# Diversity / representative selection
diverse   = dm.pick_diverse(mols, npick=100)
centroids = dm.pick_centroids(mols, npick=50)

Scale note: Butina builds a full distance matrix — fine for ~1,000 molecules, not 10,000+. Clustering parameters: see references/core_api.md.

6. Scaffold Analysis

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# Bemis-Murcko scaffold (core ring systems + linkers)
scaffold = dm.to_scaffold_murcko(mol)
scaffold_smiles = dm.to_smiles(scaffold)

Scaffold frequency counting, scaffold-to-molecule grouping, and scaffold-based train/test splitting for ML: see references/workflow_recipes.md. fuzzy_scaffolding and more: see references/fragments_scaffolds.md.

7. Molecular Fragmentation

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# BRICS (16 bond types) and RECAP (11 bond types) both return SMILES with
# attachment points like '[1*]CCN'
frags_brics = dm.fragment.brics(mol)
frags_recap = dm.fragment.recap(mol)

Cross-library fragment frequency analysis and fragment-overlap scoring recipes: see references/workflow_recipes.md. MMPA fragmentation and a method comparison table: see references/fragments_scaffolds.md.

8. 3D Conformer Generation

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# Generate 3D conformers (ETKDGv3 recommended; UFF minimization on by default)
mol_3d = dm.conformers.generate(mol, n_confs=50, rms_cutoff=0.5,
                                minimize_energy=True, method='ETKDGv3')
mol_3d.GetNumConformers()
conf = mol_3d.GetConformer(0)
positions = conf.GetPositions()          # Nx3 array of atom coordinates

# Cluster conformers by RMSD and take representatives
clusters  = dm.conformers.cluster(mol_3d, rms_cutoff=1.0, centroids=False)
centroids = dm.conformers.return_centroids(mol_3d, clusters)

# Solvent accessible surface area
sasa_values = dm.conformers.sasa(mol_3d, n_jobs=-1)
sasa = mol_3d.GetConformer(0).GetDoubleProp('rdkit_free_sasa')

Embedding methods, RMSD matrices, and low-level coordinate manipulation: see references/conformers_module.md.

9. Visualization

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# Grid image (PNG by default; use_svg=True for publications)
dm.viz.to_image(mols[:20], legends=[dm.to_smiles(m) for m in mols[:20]],
                n_cols=5, mol_size=(300, 300))
dm.viz.to_image(mols, outfile="molecules.png")
dm.viz.to_image(mols, outfile="molecules.svg", use_svg=True)

# Align by MCS for SAR series; highlight atoms/bonds; render conformers
dm.viz.to_image(similar_mols, align=True, legends=activity_labels, n_cols=4)
dm.viz.to_image(mol, highlight_atom=[0, 1, 2, 3], highlight_bond=[0, 1, 2])
dm.viz.conformers(mol_3d, n_confs=10, align_conf=True, n_cols=3)

Full to_image / conformers / circle_grid parameters and best practices: see references/descriptors_viz.md.

10. Chemical Reactions

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from rdkit.Chem import rdChemReactions

# Build a reaction from SMARTS, then apply it to a reactant tuple
rxn = rdChemReactions.ReactionFromSmarts('[C:1](=[O:2])[OH:3]>>[C:1](=[O:2])[Cl:3]')
product = dm.reactions.apply_reaction(rxn, (dm.to_mol("CC(=O)O"),), sanitize=True)
product_smiles = dm.to_smiles(product)

Batch reaction application, common reaction templates (amide, Suzuki, esterification), and the toy datamol.data datasets: see references/reactions_data.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.

Common Workflows and Patterns

Full copy-ready worked pipelines — data loading → filtering → analysis, Structure-Activity Relationship (SAR) analysis, and virtual screening — plus machine-learning feature generation and robust error-handling wrappers, have moved out of this file to keep it lean. See references/workflow_recipes.md.

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
• references/workflow_recipes.md: End-to-end pipelines, SAR/screening recipes, ML integration, error handling

Best Practices

1. Always standardize molecules from external sources:

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

2. Check for None values after molecule parsing:

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

3. Use parallel processing for large datasets:

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

4. Leverage fsspec for cloud storage:

   pythonCopy code
   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

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: Ensure fsspec and appropriate cloud provider libraries are installed (s3fs, gcsfs, etc.)

Additional Resources

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

Part of the AlterLab Academic Skills suite.