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:

uv pip install datamol

Import convention:

import datamol as dm

Core Workflows

1. Basic Molecule Handling

Creating molecules from SMILES:

import datamol as dm
Single molecule

mol = dm.to_mol("CCO")  # Ethanol
From list of SMILES

smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"]
mols = [dm.to_mol(smi) for smi in smiles_list]
Error handling

mol = dm.to_mol("invalid_smiles")  # Returns None
if mol is None:
    print("Failed to parse SMILES")

Converting molecules to SMILES:

# Canonical SMILES
smiles = dm.to_smiles(mol)
Isomeric SMILES (includes stereochemistry)

smiles = dm.to_smiles(mol, isomeric=True)
Other formats

inchi = dm.to_inchi(mol)
inchikey = dm.to_inchikey(mol)
selfies = dm.to_selfies(mol)

Standardization and sanitization (always recommend for user-provided molecules):

# Sanitize molecule
mol = dm.sanitize_mol(mol)
Full standardization (recommended for datasets)

mol = dm.standardize_mol(
    mol,
    disconnect_metals=True,
    normalize=True,
    reionize=True
)
For SMILES strings directly

clean_smiles = dm.standardize_smiles(smiles)

2. Reading and Writing Molecular Files

Refer to references/io_module.md for comprehensive I/O documentation.

Reading files:

# SDF files (most common in chemistry)
df = dm.read_sdf("compounds.sdf", mol_column='mol')
SMILES files

df = dm.read_smi("molecules.smi", smiles_column='smiles', mol_column='mol')
CSV with SMILES column

df = dm.read_csv("data.csv", smiles_column="SMILES", mol_column="mol")
Excel files

df = dm.read_excel("compounds.xlsx", sheet_name=0, mol_column="mol")
Universal reader (auto-detects format)

df = dm.open_df("file.sdf")  # Works with .sdf, .csv, .xlsx, .parquet, .json

Writing files:

# Save as SDF
dm.to_sdf(mols, "output.sdf")
Or from DataFrame

dm.to_sdf(df, "output.sdf", mol_column="mol")
Save as SMILES file

dm.to_smi(mols, "output.smi")
Excel with rendered molecule images

dm.to_xlsx(df, "output.xlsx", mol_columns=["mol"])

Remote file support (S3, GCS, HTTP):

# Read from cloud storage
df = dm.read_sdf("s3://bucket/compounds.sdf")
df = dm.read_csv("https://example.com/data.csv")
Write to cloud storage

dm.to_sdf(mols, "s3://bucket/output.sdf")

3. Molecular Descriptors and Properties

Refer to references/descriptors_viz.md for detailed descriptor documentation.

Computing descriptors for a single molecule:

# Get standard descriptor set
descriptors = dm.descriptors.compute_many_descriptors(mol)
Returns: {'mw': 46.07, 'logp': -0.03, 'hbd': 1, 'hba': 1,

          'tpsa': 20.23, 'n_aromatic_atoms': 0, ...}

Batch descriptor computation (recommended for datasets):

# Compute for all molecules in parallel
desc_df = dm.descriptors.batch_compute_many_descriptors(
    mols,
    n_jobs=-1,      # Use all CPU cores
    progress=True   # Show progress bar
)

Specific descriptors:

# Aromaticity
n_aromatic = dm.descriptors.n_aromatic_atoms(mol)
aromatic_ratio = dm.descriptors.n_aromatic_atoms_proportion(mol)
Stereochemistry

n_stereo = dm.descriptors.n_stereo_centers(mol)
n_unspec = dm.descriptors.n_stereo_centers_unspecified(mol)
Flexibility

n_rigid = dm.descriptors.n_rigid_bonds(mol)

Drug-likeness filtering (Lipinski's Rule of Five):

# Filter compounds
def is_druglike(mol):
    desc = dm.descriptors.compute_many_descriptors(mol)
    return (
        desc['mw'] <= 500 and
        desc['logp'] <= 5 and
        desc['hbd'] <= 5 and
        desc['hba'] <= 10
    )
druglike_mols = [mol for mol in mols if is_druglike(mol)]

4. Molecular Fingerprints and Similarity

Generating fingerprints:

# ECFP (Extended Connectivity Fingerprint, default)
fp = dm.to_fp(mol, fp_type='ecfp', radius=2, n_bits=2048)
Other fingerprint types

fp_maccs = dm.to_fp(mol, fp_type='maccs')
fp_topological = dm.to_fp(mol, fp_type='topological')
fp_atompair = dm.to_fp(mol, fp_type='atompair')

Similarity calculations:

# Pairwise distances within a set
distance_matrix = dm.pdist(mols, n_jobs=-1)
Distances between two sets

distances = dm.cdist(query_mols, library_mols, n_jobs=-1)
Find most similar molecules

from scipy.spatial.distance import squareform
dist_matrix = squareform(dm.pdist(mols))
Lower distance = higher similarity (Tanimoto distance = 1 - Tanimoto similarity)

5. Clustering and Diversity Selection

Refer to references/core_api.md for clustering details.

Butina clustering:

# Cluster molecules by structural similarity
clusters = dm.cluster_mols(
    mols,
    cutoff=0.2,    # Tanimoto distance threshold (0=identical, 1=completely different)
    n_jobs=-1      # Parallel processing
)
Each cluster is a list of molecule indices

for i, cluster in enumerate(clusters):
    print(f"Cluster {i}: {len(cluster)} molecules")
    cluster_mols = [mols[idx] for idx in cluster]

Important: Butina clustering builds a full distance matrix - suitable for ~1000 molecules, not for 10,000+.

Diversity selection:

# Pick diverse subset
diverse_mols = dm.pick_diverse(
    mols,
    npick=100  # Select 100 diverse molecules
)
Pick cluster centroids

centroids = dm.pick_centroids(
    mols,
    npick=50   # Select 50 representative molecules
)

6. Scaffold Analysis

Refer to references/fragments_scaffolds.md for complete scaffold documentation.

Extracting Murcko scaffolds:

# Get Bemis-Murcko scaffold (core structure)
scaffold = dm.to_scaffold_murcko(mol)
scaffold_smiles = dm.to_smiles(scaffold)

Scaffold-based analysis:

# Group compounds by scaffold
from collections import Counter
scaffolds = [dm.to_scaffold_murcko(mol) for mol in mols]
scaffold_smiles = [dm.to_smiles(s) for s in scaffolds]
Count scaffold frequency

scaffold_counts = Counter(scaffold_smiles)
most_common = scaffold_counts.most_common(10)
Create scaffold-to-molecules mapping

scaffold_groups = {}
for mol, scaf_smi in zip(mols, scaffold_smiles):
    if scaf_smi not in scaffold_groups:
        scaffold_groups[scaf_smi] = []
    scaffold_groups[scaf_smi].append(mol)

Scaffold-based train/test splitting (for ML):

# Ensure train and test sets have different scaffolds
scaffold_to_mols = {}
for mol, scaf in zip(mols, scaffold_smiles):
    if scaf not in scaffold_to_mols:
        scaffold_to_mols[scaf] = []
    scaffold_to_mols[scaf].append(mol)
Split scaffolds into train/test

import random
scaffolds = list(scaffold_to_mols.keys())
random.shuffle(scaffolds)
split_idx = int(0.8  len(scaffolds))
train_scaffolds = scaffolds[:split_idx]
test_scaffolds = scaffolds[split_idx:]
Get molecules for each split

train_mols = [mol for scaf in train_scaffolds for mol in scaffold_to_mols[scaf]]
test_mols = [mol for scaf in test_scaffolds for mol in scaffold_to_mols[scaf]]

7. Molecular Fragmentation

Refer to references/fragments_scaffolds.md for fragmentation details.

BRICS fragmentation (16 bond types):

# Fragment molecule
fragments = dm.fragment.brics(mol)
Returns: set of fragment SMILES with attachment points like '[1]CCN'

RECAP fragmentation (11 bond types):

fragments = dm.fragment.recap(mol)

Fragment analysis:

# Find common fragments across compound library
from collections import Counter
all_fragments = []
for mol in mols:
    frags = dm.fragment.brics(mol)
    all_fragments.extend(frags)
fragment_counts = Counter(all_fragments)
common_frags = fragment_counts.most_common(20)
Fragment-based scoring

def fragment_score(mol, reference_fragments):
    mol_frags = dm.fragment.brics(mol)
    overlap = mol_frags.intersection(reference_fragments)
    return len(overlap) / len(mol_frags) if mol_frags else 0

8. 3D Conformer Generation

Refer to references/conformers_module.md for detailed conformer documentation.

Generating conformers:

# Generate 3D conformers
mol_3d = dm.conformers.generate(
    mol,
    n_confs=50,           # Number to generate (auto if None)
    rms_cutoff=0.5,       # Filter similar conformers (Ångströms)
    minimize_energy=True,  # Minimize with UFF force field
    method='ETKDGv3'      # Embedding method (recommended)
)
Access conformers

n_conformers = mol_3d.GetNumConformers()
conf = mol_3d.GetConformer(0)  # Get first conformer
positions = conf.GetPositions()  # Nx3 array of atom coordinates

Conformer clustering:

# Cluster conformers by RMSD
clusters = dm.conformers.cluster(
    mol_3d,
    rms_cutoff=1.0,
    centroids=False
)
Get representative conformers

centroids = dm.conformers.return_centroids(mol_3d, clusters)

SASA calculation:

# Calculate solvent accessible surface area
sasa_values = dm.conformers.sasa(mol_3d, n_jobs=-1)
Access SASA from conformer properties

conf = mol_3d.GetConformer(0)
sasa = conf.GetDoubleProp('rdkit_free_sasa')

9. Visualization

Refer to references/descriptors_viz.md for visualization documentation.

Basic molecule grid:

# Visualize molecules
dm.viz.to_image(
    mols[:20],
    legends=[dm.to_smiles(m) for m in mols[:20]],
    n_cols=5,
    mol_size=(300, 300)
)
Save to file

dm.viz.to_image(mols, outfile="molecules.png")
SVG for publications

dm.viz.to_image(mols, outfile="molecules.svg", use_svg=True)

Aligned visualization (for SAR analysis):

# Align molecules by common substructure
dm.viz.to_image(
    similar_mols,
    align=True,  # Enable MCS alignment
    legends=activity_labels,
    n_cols=4
)

Highlighting substructures:

# Highlight specific atoms and bonds
dm.viz.to_image(
    mol,
    highlight_atom=[0, 1, 2, 3],  # Atom indices
    highlight_bond=[0, 1, 2]      # Bond indices
)

Conformer visualization:

# Display multiple conformers
dm.viz.conformers(
    mol_3d,
    n_confs=10,
    align_conf=True,
    n_cols=3
)

10. Chemical Reactions

Refer to references/reactions_data.md for reactions documentation.

Applying reactions:

from rdkit.Chem import rdChemReactions
Define reaction from SMARTS

rxn_smarts = 'C:1[OH:3]>>C:1[Cl:3]'
rxn = rdChemReactions.ReactionFromSmarts(rxn_smarts)
Apply to molecule

reactant = dm.to_mol("CC(=O)O")  # Acetic acid
product = dm.reactions.apply_reaction(
    rxn,
    (reactant,),
    sanitize=True
)
Convert to SMILES

product_smiles = dm.to_smiles(product)

Batch reaction application:

# Apply reaction to library
products = []
for mol in reactant_mols:
    try:
        prod = dm.reactions.apply_reaction(rxn, (mol,))
        if prod is not None:
            products.append(prod)
    except Exception as e:
        print(f"Reaction failed: {e}")

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

Complete Pipeline: Data Loading → Filtering → Analysis

import datamol as dm
import pandas as pd
1. Load molecules

df = dm.read_sdf("compounds.sdf")
2. Standardize

df['mol'] = df['mol'].apply(lambda m: dm.standardize_mol(m) if m else None)
df = df[df['mol'].notna()]  # Remove failed molecules
3. Compute descriptors

desc_df = dm.descriptors.batch_compute_many_descriptors(
    df['mol'].tolist(),
    n_jobs=-1,
    progress=True
)
4. Filter by drug-likeness

druglike = (
    (desc_df['mw'] <= 500) &
    (desc_df['logp'] <= 5) &
    (desc_df['hbd'] <= 5) &
    (desc_df['hba'] <= 10)
)
filtered_df = df[druglike]
5. Cluster and select diverse subset

diverse_mols = dm.pick_diverse(
    filtered_df['mol'].tolist(),
    npick=100
)
6. Visualize results

dm.viz.to_image(
    diverse_mols,
    legends=[dm.to_smiles(m) for m in diverse_mols],
    outfile="diverse_compounds.png",
    n_cols=10
)

Structure-Activity Relationship (SAR) Analysis

# Group by scaffold
scaffolds = [dm.to_scaffold_murcko(mol) for mol in mols]
scaffold_smiles = [dm.to_smiles(s) for s in scaffolds]
Create DataFrame with activities

sar_df = pd.DataFrame({
    'mol': mols,
    'scaffold': scaffold_smiles,
    'activity': activities  # User-provided activity data
})
Analyze each scaffold series

for scaffold, group in sar_df.groupby('scaffold'):
    if len(group) >= 3:  # Need multiple examples
        print(f"\nScaffold: {scaffold}")
        print(f"Count: {len(group)}")
        print(f"Activity range: {group['activity'].min():.2f} - {group['activity'].max():.2f}")
        # Visualize with activities as legends
        dm.viz.to_image(
            group['mol'].tolist(),
            legends=[f"Activity: {act:.2f}" for act in group['activity']],
            align=True  # Align by common substructure
        )

Virtual Screening Pipeline

# 1. Generate fingerprints for query and library
query_fps = [dm.to_fp(mol) for mol in query_actives]
library_fps = [dm.to_fp(mol) for mol in library_mols]
2. Calculate similarities

from scipy.spatial.distance import cdist
import numpy as np
distances = dm.cdist(query_actives, library_mols, n_jobs=-1)
3. Find closest matches (min distance to any query)

min_distances = distances.min(axis=0)
similarities = 1 - min_distances  # Convert distance to similarity
4. Rank and select top hits

top_indices = np.argsort(similarities)[::-1][:100]  # Top 100
top_hits = [library_mols[i] for i in top_indices]
top_scores = [similarities[i] for i in top_indices]
5. Visualize hits

dm.viz.to_image(
    top_hits[:20],
    legends=[f"Sim: {score:.3f}" for score in top_scores[:20]],
    outfile="screening_hits.png"
)

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

Always standardize molecules from external sources:

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

Check for None values after molecule parsing:

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

Use parallel processing for large datasets:

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

Leverage fsspec for cloud storage:

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

Use appropriate fingerprints for similarity:

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

Consider scale limitations:

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

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

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

# 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

from sklearn.ensemble import RandomForestRegressor
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: 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

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