molfeat-molecular-featurization

name	molfeat-molecular-featurization
description	Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
license	Apache-2.0

Molfeat — Molecular Featurization Hub

Overview

Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers under a scikit-learn compatible API. Convert SMILES strings into numerical representations (fingerprints, descriptors, deep learning embeddings) for QSAR modeling, virtual screening, similarity searching, and chemical space analysis.

When to Use

• Building QSAR/QSPR models requiring molecular features as input
• Virtual screening — ranking compound libraries by predicted activity
• Similarity searching against molecular databases
• Chemical space analysis — clustering, visualization, dimensionality reduction
• Deep learning on molecules using pretrained embeddings (ChemBERTa, GIN)
• Featurization pipelines integrating with scikit-learn or PyTorch
• Comparing multiple molecular representations for benchmarking
• For molecular manipulation and filtering use datamol instead; for substructure-based molecular operations use rdkit-cheminformatics

Prerequisites

uv pip install molfeat

# Optional extras for specific featurizer types
uv pip install "molfeat[transformer]"   # ChemBERTa, ChemGPT, MolT5
uv pip install "molfeat[dgl]"           # GIN graph neural networks
uv pip install "molfeat[graphormer]"    # Graphormer models
uv pip install "molfeat[fcd]"           # FCD descriptors
uv pip install "molfeat[map4]"          # MAP4 fingerprints
uv pip install "molfeat[all]"           # All dependencies

Quick Start

from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer

smiles = ["CCO", "CC(=O)O", "c1ccccc1", "CC(C)O"]

# Create fingerprint calculator + transformer
calc = FPCalculator("ecfp", radius=3, fpSize=2048)
transformer = MoleculeTransformer(calc, n_jobs=-1)

# Featurize batch in parallel
features = transformer(smiles)
print(f"Shape: {features.shape}")  # (4, 2048)

# Save configuration for reproducibility
transformer.to_state_yaml_file("featurizer_config.yml")

Key Concepts

Architecture: Calculator → Transformer → Store

Molfeat organizes featurization into three layers:

Layer	Class	Purpose	Use When
Calculator	molfeat.calc.*	Single molecule → feature vector	Custom loops, single molecules
Transformer	molfeat.trans.MoleculeTransformer	Batch processing with parallelization	Datasets, scikit-learn pipelines
Store	molfeat.store.ModelStore	Discovery and loading of pretrained models	Finding available featurizers

Calculators are callable: calc("CCO") returns a numpy array. Transformers wrap calculators for batch processing: transformer(smiles_list) returns a 2D array. Pretrained transformers (PretrainedMolTransformer) add batched GPU inference and caching.

Featurizer Selection Guide

Task	Recommended	Dimensions	Speed
General QSAR	ecfp (radius=3)	2048	Fast
Scaffold similarity	maccs	167	Very fast
Large-scale screening	map4	1024	Fast
Interpretable models	desc2D (RDKitDescriptors2D)	200+	Fast
Comprehensive descriptors	mordred	1800+	Medium
Transfer learning	ChemBERTa-77M-MLM	768	Slow*
Graph-based DL	gin-supervised-masking	Variable	Slow*
Pharmacophore	fcfp or cats2D	2048 / 21	Fast
3D shape	usr / usrcat	12 / 60	Fast

*First run slow; subsequent runs cached.

State Persistence

Save and reload exact featurizer configuration for reproducibility:

# Save
transformer.to_state_yaml_file("config.yml")
transformer.to_state_json_file("config.json")

# Reload
loaded = MoleculeTransformer.from_state_yaml_file("config.yml")

Core API

1. Fingerprint Calculators

from molfeat.calc import FPCalculator

# ECFP — most popular, general-purpose
ecfp = FPCalculator("ecfp", radius=3, fpSize=2048)
fp = ecfp("CCO")
print(f"ECFP shape: {fp.shape}")  # (2048,)

# MACCS keys — 167-bit structural keys, fast scaffold similarity
maccs = FPCalculator("maccs")
fp = maccs("c1ccccc1")
print(f"MACCS shape: {fp.shape}")  # (167,)

# Count-based fingerprints (non-binary)
ecfp_count = FPCalculator("ecfp-count", radius=3, fpSize=2048)

# MAP4 — MinHashed atom-pair, efficient for large databases
map4 = FPCalculator("map4")
print(f"MAP4 shape: {map4('CCO').shape}")  # (1024,)

Available fingerprint types: ecfp, fcfp, maccs, rdkit, avalon, pattern, layered, atompair, topological, map4, secfp, erg, estate (and count variants with -count suffix).

2. Descriptor Calculators

from molfeat.calc import RDKitDescriptors2D, MordredDescriptors

# RDKit 2D — 200+ named properties (MW, logP, TPSA, etc.)
desc2d = RDKitDescriptors2D()
descriptors = desc2d("CCO")
print(f"2D descriptors: {len(descriptors)}")  # 200+
print(f"Feature names: {desc2d.columns[:5]}")

# Mordred — 1800+ comprehensive descriptors
mordred = MordredDescriptors()
descriptors = mordred("c1ccccc1O")
print(f"Mordred descriptors: {len(descriptors)}")  # 1800+

3. Pharmacophore & Shape Calculators

from molfeat.calc import CATSCalculator, USRDescriptors

# CATS — pharmacophore point pair distributions
cats = CATSCalculator(mode="2D", scale="raw")
descriptors = cats("CC(C)Cc1ccc(C)cc1C")
print(f"CATS shape: {descriptors.shape}")  # (21,)

# USR — ultrafast shape recognition
usr = USRDescriptors()
shape = usr("CC(=O)Oc1ccccc1C(=O)O")
print(f"USR shape: {shape.shape}")  # (12,)

4. Batch Processing with Transformers

from molfeat.trans import MoleculeTransformer, FeatConcat
from molfeat.calc import FPCalculator

smiles = ["CCO", "CC(=O)O", "c1ccccc1", "CC(C)O", "CCCC"]

# Parallel batch processing
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
features = transformer(smiles)
print(f"Batch shape: {features.shape}")  # (5, 2048)

# Concatenate multiple featurizers
concat = FeatConcat([
    FPCalculator("maccs"),      # 167 dims
    FPCalculator("ecfp")        # 2048 dims
])
combo_transformer = MoleculeTransformer(concat, n_jobs=-1)
combo_features = combo_transformer(smiles)
print(f"Combined shape: {combo_features.shape}")  # (5, 2215)

# Error-tolerant processing
safe_transformer = MoleculeTransformer(
    FPCalculator("ecfp"), n_jobs=-1,
    ignore_errors=True, verbose=True
)
features = safe_transformer(["CCO", "invalid", "c1ccccc1"])
# Returns None for failed molecules

5. Pretrained Model Embeddings

from molfeat.trans.pretrained import PretrainedMolTransformer

# ChemBERTa — RoBERTa trained on 77M PubChem compounds
chemberta = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)
embeddings = chemberta(["CCO", "CC(=O)O", "c1ccccc1"])
print(f"ChemBERTa shape: {embeddings.shape}")  # (3, 768)

# GIN — graph neural network pretrained on ChEMBL
gin = PretrainedMolTransformer("gin-supervised-masking", n_jobs=-1)
graph_emb = gin(["CCO", "CC(=O)O"])
print(f"GIN shape: {graph_emb.shape}")

6. ModelStore — Discovering Featurizers

from molfeat.store.modelstore import ModelStore

store = ModelStore()
print(f"Total available: {len(store.available_models)}")

# Search for specific model
results = store.search(name="ChemBERTa")
for model in results:
    print(f"  {model.name}: {model.description}")

# View usage and load
card = store.search(name="ChemBERTa-77M-MLM")[0]
card.usage()
transformer = store.load("ChemBERTa-77M-MLM")

Common Workflows

Workflow 1: QSAR Model Building

from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score

# Featurize molecules
transformer = MoleculeTransformer(FPCalculator("ecfp", radius=3), n_jobs=-1)
X = transformer(smiles_train)
print(f"Features shape: {X.shape}")

# Train and evaluate
model = RandomForestRegressor(n_estimators=100)
scores = cross_val_score(model, X, y_train, cv=5, scoring='r2')
print(f"R² = {scores.mean():.3f} ± {scores.std():.3f}")

# Save for deployment
transformer.to_state_yaml_file("production_featurizer.yml")

Workflow 2: Virtual Screening Pipeline

from sklearn.ensemble import RandomForestClassifier

# Step 1: Featurize known actives/inactives
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X_train = transformer(train_smiles)

# Step 2: Train classifier
clf = RandomForestClassifier(n_estimators=500, n_jobs=-1)
clf.fit(X_train, train_labels)

# Step 3: Screen library (e.g., 1M compounds)
X_screen = transformer(screening_smiles)
predictions = clf.predict_proba(X_screen)[:, 1]

# Step 4: Rank and select top hits
top_indices = predictions.argsort()[::-1][:1000]
top_hits = [screening_smiles[i] for i in top_indices]
print(f"Top 1000 hits selected from {len(screening_smiles)} compounds")

Workflow 3: Featurizer Benchmarking

from molfeat.calc import FPCalculator, RDKitDescriptors2D
from sklearn.metrics import roc_auc_score

featurizers = {
    'ECFP': FPCalculator("ecfp"),
    'MACCS': FPCalculator("maccs"),
    'Descriptors': RDKitDescriptors2D(),
}

for name, calc in featurizers.items():
    transformer = MoleculeTransformer(calc, n_jobs=-1)
    X_train = transformer(smiles_train)
    X_test = transformer(smiles_test)
    clf = RandomForestClassifier(n_estimators=100)
    clf.fit(X_train, y_train)
    auc = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
    print(f"{name}: AUC = {auc:.3f}")

Common Recipes

Recipe: Scikit-learn Pipeline Integration

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier

pipeline = Pipeline([
    ('featurizer', MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)),
    ('classifier', RandomForestClassifier(n_estimators=100))
])
pipeline.fit(smiles_train, y_train)
predictions = pipeline.predict(smiles_test)

Recipe: Similarity Search

from sklearn.metrics.pairwise import cosine_similarity

calc = FPCalculator("ecfp")
query_fp = calc("CC(=O)Oc1ccccc1C(=O)O").reshape(1, -1)  # Aspirin

transformer = MoleculeTransformer(calc, n_jobs=-1)
db_fps = transformer(database_smiles)

similarities = cosine_similarity(query_fp, db_fps)[0]
top_k = similarities.argsort()[-10:][::-1]
for i in top_k:
    print(f"  {database_smiles[i]}: {similarities[i]:.3f}")

Recipe: Chunk Processing for Large Datasets

import numpy as np

def featurize_chunks(smiles_list, transformer, chunk_size=10000):
    all_features = []
    for i in range(0, len(smiles_list), chunk_size):
        chunk = smiles_list[i:i+chunk_size]
        features = transformer(chunk)
        all_features.append(features)
        print(f"Processed {min(i+chunk_size, len(smiles_list))}/{len(smiles_list)}")
    return np.vstack(all_features)

Key Parameters

Parameter	Module	Default	Description
method	FPCalculator	—	Fingerprint type: ecfp, maccs, map4, etc.
radius	FPCalculator	3	Circular fingerprint radius
fpSize	FPCalculator	2048	Fingerprint bit length
counting	FPCalculator	False	Count vector instead of binary
n_jobs	MoleculeTransformer	1	Parallel workers (-1 = all cores)
ignore_errors	MoleculeTransformer	False	Skip invalid molecules (returns None)
verbose	MoleculeTransformer	False	Log processing details
dtype	MoleculeTransformer	float64	Output type (float32 for memory)
mode	CATSCalculator	"2D"	Distance calculation mode
scale	CATSCalculator	"raw"	Scaling: raw, num, count

Best Practices

• Use n_jobs=-1 for parallel processing on all CPU cores — significant speedup for batch featurization
• Start with ECFP for initial baselines — best general-purpose fingerprint before trying deep learning
• Use ignore_errors=True for large datasets — invalid SMILES won't crash the pipeline
• Save configurations with to_state_yaml_file() for reproducibility — recreate exact featurizer later
• Use float32 when memory matters: MoleculeTransformer(calc, dtype=np.float32)
• Cache pretrained embeddings — first ChemBERTa/GIN inference is slow, subsequent runs use cache
• Process in chunks for datasets >100K — prevents memory exhaustion (see Recipes)
• Combine fingerprints with FeatConcat to capture complementary molecular information

Troubleshooting

Problem	Cause	Solution
ValueError: unsupported featurizer	Unknown method name	Check FPCalculator supported types or use ModelStore.search()
ImportError for pretrained model	Missing optional dependency	Install extras: pip install "molfeat[transformer]" or "molfeat[dgl]"
None in output array	Invalid SMILES with ignore_errors=True	Filter results: [f for f in features if f is not None]
Memory error on large dataset	Too many molecules at once	Process in chunks of 10K-50K (see Recipes)
Slow pretrained model inference	First run downloads model weights	Normal — subsequent runs use cache
Shape mismatch in pipeline	Mixed valid/invalid molecules	Ensure ignore_errors=True and filter None before ML model
Reproducibility issues	Different molfeat versions	Pin version and save config: transformer.to_state_yaml_file()

Related Skills

• datamol-cheminformatics — High-level molecular manipulation (standardization, I/O, conformers)
• rdkit-cheminformatics — Low-level cheminformatics (substructure, reactions, 3D)
• scikit-learn — ML models consuming molfeat features

References

• Official documentation: https://molfeat-docs.datamol.io/
• GitHub repository: https://github.com/datamol-io/molfeat
• PyPI package: https://pypi.org/project/molfeat/
• Tutorial: https://portal.valencelabs.com/datamol/post/types-of-featurizers-b1e8HHrbFMkbun6

Bundled Resources

Main SKILL.md + 2 reference files. Original total: 1,273 lines (SKILL.md 510 + api_reference.md 429 + available_featurizers.md 334). Scripts: none. Examples: 724 lines (examples.md).

references/available_featurizers.md: Complete catalog of all 100+ featurizers organized by category — transformer models, GNNs, descriptors, fingerprints, pharmacophore, shape, scaffold, graph featurizers. Includes dimensions, dependencies, and selection guidance per category. Purely lookup-oriented content preserved as reference.

references/api_reference.md: Detailed API reference for molfeat.calc, molfeat.trans, and molfeat.store modules. Covers SerializableCalculator base class, all calculator subclasses with parameters, MoleculeTransformer methods, PretrainedMolTransformer, FeatConcat, ModelStore/ModelCard API, data type control, and PyTorch integration patterns.

Original file disposition:

• SKILL.md (510 lines) → Core API modules 1-6, Key Concepts (architecture, selection guide), Quick Start, Workflows 1-3. "Choosing the Right Featurizer" → Key Concepts selection guide table. "Advanced Features" (custom preprocessing, batch processing, caching) → Recipes + Best Practices. "Common Featurizers Reference" table → Key Concepts selection guide. "Performance Tips" → Best Practices. Per-use-case disposition: QSAR Modeling → Workflow 1, Virtual Screening → Workflow 2, Similarity Search → Recipe, Chemical Space → When to Use bullet, scikit-learn Pipeline → Recipe, Featurizer Comparison → Workflow 3
• references/api_reference.md (429 lines) → Migrated to new references/api_reference.md. Core patterns (FPCalculator, MoleculeTransformer, basic ModelStore) relocated to SKILL.md Core API modules 1-6. Detailed class methods, SerializableCalculator base class, PrecomputedMolTransformer, and PyTorch integration retained in reference
• references/available_featurizers.md (334 lines) → Migrated to new references/available_featurizers.md. Top-level summary → Key Concepts selection guide table. Full categorized catalog retained in reference
• references/examples.md (724 lines) → Fully consolidated inline: installation → Prerequisites; calculator examples → Core API 1-3; transformer examples → Core API 4; pretrained examples → Core API 5; ML integration → Workflows 1-3 + Recipes; advanced patterns (custom preprocessing, caching, chunk processing) → Recipes + Best Practices; troubleshooting → Troubleshooting table. No separate reference file needed — all content absorbed into SKILL.md sections

Retention: ~490 lines (SKILL.md) + ~170 lines (available_featurizers) + ~190 lines (api_reference) = ~850 / 1,273 original (excl. examples.md treated as consolidated) = ~67%. Including examples.md in denominator: ~850 / 1,997 = ~43%.