Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
SKILL.md
Molfeat - Molecular Featurization Hub
Overview
Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.
Version note: Examples target molfeat 0.11.0 (PyPI stable, May 2025). Requires Python 3.9–3.10 (requires-python caps below 3.11). Depends on datamol ≥0.8.0 and PyTorch ≥1.13. Since 0.8.7, prefer datamol Mol objects over raw rdkit.Chem.Mol. Since 0.10.1, fingerprint calculators use RDKit's rdFingerprintGenerator API internally. Since 0.11.0, pretrained models load in memory and base models are set to PyTorch evaluation mode automatically.
When to Use This Skill
This skill should be used when working with:
Molecular machine learning: Building QSAR/QSPR models, property prediction
Virtual screening: Ranking compound libraries for biological activity
Similarity searching: Finding structurally similar molecules
Chemical space analysis: Clustering, visualization, dimensionality reduction
Deep learning: Training neural networks on molecular data
Featurization pipelines: Converting SMILES to ML-ready representations
Cheminformatics: Any task requiring molecular feature extraction
Installation
Use a Python 3.9 or 3.10 environment (molfeat does not install on 3.11+ as of 0.11.0):
uv pip install "molfeat==0.11.0"
# With all pip-installable optional dependencies
uv pip install "molfeat[all]==0.11.0"
External featurizers: MAP4 is not bundled in molfeat extras — install from reymond-group/map4 separately. Some heavy deps (DGL, dgllife, graphormer-pretrained) are easier via conda-forge; see optional dependencies.
Core Concepts
Molfeat organizes featurization into three hierarchical classes:
1. Calculators (molfeat.calc)
Callable objects that convert individual molecules into feature vectors. Accept RDKit Chem.Mol objects or SMILES strings.
Use calculators for:
Single molecule featurization
Custom processing loops
Direct feature computation
Example:
from molfeat.calc import FPCalculator
calc = FPCalculator("ecfp", radius=3, fpSize=2048)
features = calc("CCO") # Returns numpy array (2048,)
2. Transformers (molfeat.trans)
Scikit-learn compatible transformers that wrap calculators for batch processing with parallelization.
Use transformers for:
Batch featurization of molecular datasets
Integration with scikit-learn pipelines
Parallel processing (automatic CPU utilization)
Example:
from molfeat.trans import MoleculeTransformer
from molfeat.calc import FPCalculator
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
features = transformer(smiles_list) # Parallel processing
Specialized transformers for deep learning models with batched inference and caching.
Use pretrained transformers for:
State-of-the-art molecular embeddings
Transfer learning from large chemical datasets
Deep learning feature extraction
Example:
from molfeat.trans.pretrained import PretrainedMolTransformer
transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)
embeddings = transformer(smiles_list) # Deep learning embeddings
Quick Start Workflow
Basic Featurization
import datamol as dm
from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer
# Load molecular data
smiles = ["CCO", "CC(=O)O", "c1ccccc1", "CC(C)O"]
# Create calculator and transformer
calc = FPCalculator("ecfp", radius=3)
transformer = MoleculeTransformer(calc, n_jobs=-1)
# Featurize molecules
features = transformer(smiles)
print(f"Shape: {features.shape}") # (4, 2048)
Save and Load Configuration
# Save featurizer configuration for reproducibility
transformer.to_state_yaml_file("featurizer_config.yml")
# Reload exact configuration
loaded = MoleculeTransformer.from_state_yaml_file("featurizer_config.yml")
Handle Errors Gracefully
# Process dataset with potentially invalid SMILES
transformer = MoleculeTransformer(
calc,
n_jobs=-1,
ignore_errors=True, # Continue on failures
verbose=True # Log error details
)
features = transformer(smiles_with_errors)
# Returns None for failed molecules
Choosing a Featurizer and Common Workflows
Featurizer choice by task — traditional ML (RF, SVM, XGBoost), deep learning, similarity
searching, and pharmacophore-based approaches — plus worked workflows for QSAR model
building, virtual screening, similarity search, scikit-learn pipeline integration, and
comparing multiple featurizers, are in
references/choosing_a_featurizer.md.
Use the ModelStore to explore all available featurizers:
from molfeat.store.modelstore import ModelStore
store = ModelStore()
# List all available models
all_models = store.available_models
print(f"Total featurizers: {len(all_models)}")
# Search for specific models
chemberta_models = store.search(name="ChemBERTa")
for model in chemberta_models:
print(f"- {model.name}: {model.description}")
# Get usage information
model_card = store.search(name="ChemBERTa-77M-MLM")[0]
model_card.usage() # Display usage examples
# Load model
transformer = store.load("ChemBERTa-77M-MLM")
import numpy as np
def featurize_in_chunks(smiles_list, transformer, chunk_size=10000):
"""Process large datasets in chunks to manage memory"""
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)
return np.vstack(all_features)
Caching Expensive Embeddings
Prefer molfeat's built-in pretrained-model cache when possible. For custom embedding caches, use NumPy arrays instead of pickle (pickle can execute arbitrary code when loading untrusted files):
import numpy as np
from pathlib import Path
cache_file = Path("embeddings_cache.npz") # fixed path under your project
transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)
if cache_file.exists():
embeddings = np.load(cache_file)["embeddings"]
else:
embeddings = transformer(smiles_list)
np.savez(cache_file, embeddings=embeddings)
Performance Tips
Use parallelization: Set n_jobs=-1 to utilize all CPU cores
Batch processing: Process multiple molecules at once instead of loops
Choose appropriate featurizers: Fingerprints are faster than deep learning models
Cache pretrained models: Leverage built-in caching for repeated use
Use float32: Set dtype=np.float32 when precision allows
Handle errors efficiently: Use ignore_errors=True for large datasets
Common Featurizers Reference
Quick reference for frequently used featurizers:
Featurizer
Type
Dimensions
Speed
Use Case
ecfp
Fingerprint
2048
Fast
General purpose
maccs
Fingerprint
167
Very fast
Scaffold similarity
desc2D
Descriptors
200+
Fast
Interpretable models
mordred
Descriptors
1800+
Medium
Comprehensive features
map4
Fingerprint
1024
Fast
Large-scale screening
ChemBERTa-77M-MLM
Deep learning
768
Slow*
Transfer learning
gin-supervised-masking
GNN
Variable
Slow*
Graph-based models
*First run is slow; subsequent runs benefit from caching
Resources
This skill includes comprehensive reference documentation:
references/api_reference.md
Complete API documentation covering:
molfeat.calc - All calculator classes and parameters
molfeat.trans - Transformer classes and methods
molfeat.store - ModelStore usage
Common patterns and integration examples
Performance optimization tips
When to load: Reference when implementing specific calculators, understanding transformer parameters, or integrating with scikit-learn/PyTorch.
references/available_featurizers.md
Comprehensive catalog of all 100+ featurizers organized by category:
Transformer-based language models (ChemBERTa, ChemGPT)
Graph neural networks (GIN, Graphormer)
Molecular descriptors (RDKit, Mordred)
Fingerprints (ECFP, MACCS, MAP4, and 15+ others)
Pharmacophore descriptors (CATS, Gobbi)
Shape descriptors (USR, ElectroShape)
Scaffold-based descriptors
When to load: Reference when selecting the optimal featurizer for a specific task, exploring available options, or understanding featurizer characteristics.
Search tip: Use grep to find specific featurizer types:
