molfeat

•

24.4k23993 months ago

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Quick Install

npxskills add davila7/claude-code-templates--skill molfeat

Instructions

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.

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

uv pip install molfeat

# With all optional dependencies
uv pip install "molfeat[all]"

Optional dependencies for specific featurizers:

molfeat[dgl] - GNN models (GIN variants)
molfeat[graphormer] - Graphormer models
molfeat[transformer] - ChemBERTa, ChemGPT, MolT5
molfeat[fcd] - FCD descriptors
molfeat[map4] - MAP4 fingerprints

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

3. Pretrained Transformers (`molfeat.trans.pretrained`)

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 the Right Featurizer

For Traditional Machine Learning (RF, SVM, XGBoost)

Start with fingerprints:

# ECFP - Most popular, general-purpose
FPCalculator("ecfp", radius=3, fpSize=2048)

# MACCS - Fast, good for scaffold hopping
FPCalculator("maccs")

# MAP4 - Efficient for large-scale screening
FPCalculator("map4")

For interpretable models:

# RDKit 2D descriptors (200+ named properties)
from molfeat.calc import RDKitDescriptors2D
RDKitDescriptors2D()

# Mordred (1800+ comprehensive descriptors)
from molfeat.calc import MordredDescriptors
MordredDescriptors()

Combine multiple featurizers:

from molfeat.trans import FeatConcat

concat = FeatConcat([
    FPCalculator("maccs"),      # 167 dimensions
    FPCalculator("ecfp")         # 2048 dimensions
])  # Result: 2215-dimensional combined features

For Deep Learning

Transformer-based embeddings:

# ChemBERTa - Pre-trained on 77M PubChem compounds
PretrainedMolTransformer("ChemBERTa-77M-MLM")

# ChemGPT - Autoregressive language model
PretrainedMolTransformer("ChemGPT-1.2B")

Graph neural networks:

# GIN models with different pre-training objectives
PretrainedMolTransformer("gin-supervised-masking")
PretrainedMolTransformer("gin-supervised-infomax")

# Graphormer for quantum chemistry
PretrainedMolTransformer("Graphormer-pcqm4mv2")

For Similarity Searching

# ECFP - General purpose, most widely used
FPCalculator("ecfp")

# MACCS - Fast, scaffold-based similarity
FPCalculator("maccs")

# MAP4 - Efficient for large databases
FPCalculator("map4")

# USR/USRCAT - 3D shape similarity
from molfeat.calc import USRDescriptors
USRDescriptors()

For Pharmacophore-Based Approaches

# FCFP - Functional group based
FPCalculator("fcfp")

# CATS - Pharmacophore pair distributions
from molfeat.calc import CATSCalculator
CATSCalculator(mode="2D")

# Gobbi - Explicit pharmacophore features
FPCalculator("gobbi2D")

Common Workflows

Building a QSAR Model

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

# Featurize molecules
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X = transformer(smiles_train)

# Train model
model = RandomForestRegressor(n_estimators=100)
scores = cross_val_score(model, X, y_train, cv=5)
print(f"R² = {scores.mean():.3f}")

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

Virtual Screening Pipeline

from sklearn.ensemble import RandomForestClassifier

# Train on known actives/inactives
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X_train = transformer(train_smiles)
clf = RandomForestClassifier(n_estimators=500)
clf.fit(X_train, train_labels)

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

# Rank and select top hits
top_indices = predictions.argsort()[::-1][:1000]
top_hits = [screening_library[i] for i in top_indices]

Similarity Search

from sklearn.metrics.pairwise import cosine_similarity

# Query molecule
calc = FPCalculator("ecfp")
query_fp = calc(query_smiles).reshape(1, -1)

# Database fingerprints
transformer = MoleculeTransformer(calc, n_jobs=-1)
database_fps = transformer(database_smiles)

# Compute similarity
similarities = cosine_similarity(query_fp, database_fps)[0]
top_similar = similarities.argsort()[-10:][::-1]

Scikit-learn Pipeline Integration

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

# Create end-to-end pipeline
pipeline = Pipeline([
    ('featurizer', MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)),
    ('classifier', RandomForestClassifier(n_estimators=100))
])

# Train and predict directly on SMILES
pipeline.fit(smiles_train, y_train)
predictions = pipeline.predict(smiles_test)

Comparing Multiple Featurizers

featurizers = {
    'ECFP': FPCalculator("ecfp"),
    'MACCS': FPCalculator("maccs"),
    'Descriptors': RDKitDescriptors2D(),
    'ChemBERTa': PretrainedMolTransformer("ChemBERTa-77M-MLM")
}

results = {}
for name, feat in featurizers.items():
    transformer = MoleculeTransformer(feat, n_jobs=-1)
    X = transformer(smiles)
    # Evaluate with your ML model
    score = evaluate_model(X, y)
    results[name] = score

Discovering Available Featurizers

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")

Advanced Features

Custom Preprocessing

class CustomTransformer(MoleculeTransformer):
    def preprocess(self, mol):
        """Custom preprocessing pipeline"""
        if isinstance(mol, str):
            mol = dm.to_mol(mol)
        mol = dm.standardize_mol(mol)
        mol = dm.remove_salts(mol)
        return mol

transformer = CustomTransformer(FPCalculator("ecfp"), n_jobs=-1)

Batch Processing Large Datasets

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

import pickle

cache_file = "embeddings_cache.pkl"
transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)

try:
    with open(cache_file, "rb") as f:
        embeddings = pickle.load(f)
except FileNotFoundError:
    embeddings = transformer(smiles_list)
    with open(cache_file, "wb") as f:
        pickle.dump(embeddings, f)

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

*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:

grep -i "chembert" references/available_featurizers.md
grep -i "pharmacophore" references/available_featurizers.md

references/examples.md

Practical code examples for common scenarios:

Installation and quick start
Calculator and transformer examples
Pretrained model usage
Scikit-learn and PyTorch integration
Virtual screening workflows
QSAR model building
Similarity searching
Troubleshooting and best practices

When to load: Reference when implementing specific workflows, troubleshooting issues, or learning molfeat patterns.

Troubleshooting

Invalid Molecules

Enable error handling to skip invalid SMILES:

transformer = MoleculeTransformer(
    calc,
    ignore_errors=True,
    verbose=True
)

Memory Issues with Large Datasets

Process in chunks or use streaming approaches for datasets > 100K molecules.

Pretrained Model Dependencies

Some models require additional packages. Install specific extras:

uv pip install "molfeat[transformer]"  # For ChemBERTa/ChemGPT
uv pip install "molfeat[dgl]"          # For GIN models

Reproducibility

Save exact configurations and document versions:

transformer.to_state_yaml_file("config.yml")
import molfeat
print(f"molfeat version: {molfeat.__version__}")

Additional Resources

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

Loading…

Related Skills

skill-creator

Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.

367,360

skill-creator

Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.

367,309

1password

Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.

365,915

himalaya

CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).

365,885

Tags & Topics

anthropicanthropic-claudeclaudeclaude-code

Molfeat - Molecular Featurization Hub

Overview

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

uv pip install molfeat

# With all optional dependencies
uv pip install "molfeat[all]"

Optional dependencies for specific featurizers:

molfeat[dgl] - GNN models (GIN variants)
molfeat[graphormer] - Graphormer models
molfeat[transformer] - ChemBERTa, ChemGPT, MolT5
molfeat[fcd] - FCD descriptors
molfeat[map4] - MAP4 fingerprints

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

3. Pretrained Transformers (`molfeat.trans.pretrained`)

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 the Right Featurizer

For Traditional Machine Learning (RF, SVM, XGBoost)

Start with fingerprints:

# ECFP - Most popular, general-purpose
FPCalculator("ecfp", radius=3, fpSize=2048)

# MACCS - Fast, good for scaffold hopping
FPCalculator("maccs")

# MAP4 - Efficient for large-scale screening
FPCalculator("map4")

For interpretable models:

# RDKit 2D descriptors (200+ named properties)
from molfeat.calc import RDKitDescriptors2D
RDKitDescriptors2D()

# Mordred (1800+ comprehensive descriptors)
from molfeat.calc import MordredDescriptors
MordredDescriptors()

Combine multiple featurizers:

from molfeat.trans import FeatConcat

concat = FeatConcat([
    FPCalculator("maccs"),      # 167 dimensions
    FPCalculator("ecfp")         # 2048 dimensions
])  # Result: 2215-dimensional combined features

For Deep Learning

Transformer-based embeddings:

# ChemBERTa - Pre-trained on 77M PubChem compounds
PretrainedMolTransformer("ChemBERTa-77M-MLM")

# ChemGPT - Autoregressive language model
PretrainedMolTransformer("ChemGPT-1.2B")

Graph neural networks:

# GIN models with different pre-training objectives
PretrainedMolTransformer("gin-supervised-masking")
PretrainedMolTransformer("gin-supervised-infomax")

# Graphormer for quantum chemistry
PretrainedMolTransformer("Graphormer-pcqm4mv2")

For Similarity Searching

# ECFP - General purpose, most widely used
FPCalculator("ecfp")

# MACCS - Fast, scaffold-based similarity
FPCalculator("maccs")

# MAP4 - Efficient for large databases
FPCalculator("map4")

# USR/USRCAT - 3D shape similarity
from molfeat.calc import USRDescriptors
USRDescriptors()

For Pharmacophore-Based Approaches

# FCFP - Functional group based
FPCalculator("fcfp")

# CATS - Pharmacophore pair distributions
from molfeat.calc import CATSCalculator
CATSCalculator(mode="2D")

# Gobbi - Explicit pharmacophore features
FPCalculator("gobbi2D")

Common Workflows

Building a QSAR Model

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

# Featurize molecules
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X = transformer(smiles_train)

# Train model
model = RandomForestRegressor(n_estimators=100)
scores = cross_val_score(model, X, y_train, cv=5)
print(f"R² = {scores.mean():.3f}")

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

Virtual Screening Pipeline

from sklearn.ensemble import RandomForestClassifier

# Train on known actives/inactives
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X_train = transformer(train_smiles)
clf = RandomForestClassifier(n_estimators=500)
clf.fit(X_train, train_labels)

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

# Rank and select top hits
top_indices = predictions.argsort()[::-1][:1000]
top_hits = [screening_library[i] for i in top_indices]

Similarity Search

from sklearn.metrics.pairwise import cosine_similarity

# Query molecule
calc = FPCalculator("ecfp")
query_fp = calc(query_smiles).reshape(1, -1)

# Database fingerprints
transformer = MoleculeTransformer(calc, n_jobs=-1)
database_fps = transformer(database_smiles)

# Compute similarity
similarities = cosine_similarity(query_fp, database_fps)[0]
top_similar = similarities.argsort()[-10:][::-1]

Scikit-learn Pipeline Integration

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

# Create end-to-end pipeline
pipeline = Pipeline([
    ('featurizer', MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)),
    ('classifier', RandomForestClassifier(n_estimators=100))
])

# Train and predict directly on SMILES
pipeline.fit(smiles_train, y_train)
predictions = pipeline.predict(smiles_test)

Comparing Multiple Featurizers

featurizers = {
    'ECFP': FPCalculator("ecfp"),
    'MACCS': FPCalculator("maccs"),
    'Descriptors': RDKitDescriptors2D(),
    'ChemBERTa': PretrainedMolTransformer("ChemBERTa-77M-MLM")
}

results = {}
for name, feat in featurizers.items():
    transformer = MoleculeTransformer(feat, n_jobs=-1)
    X = transformer(smiles)
    # Evaluate with your ML model
    score = evaluate_model(X, y)
    results[name] = score

Discovering Available Featurizers

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")

Advanced Features

Custom Preprocessing

class CustomTransformer(MoleculeTransformer):
    def preprocess(self, mol):
        """Custom preprocessing pipeline"""
        if isinstance(mol, str):
            mol = dm.to_mol(mol)
        mol = dm.standardize_mol(mol)
        mol = dm.remove_salts(mol)
        return mol

transformer = CustomTransformer(FPCalculator("ecfp"), n_jobs=-1)

Batch Processing Large Datasets

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

import pickle

cache_file = "embeddings_cache.pkl"
transformer = PretrainedMolTransformer("ChemBERTa-77M-MLM", n_jobs=-1)

try:
    with open(cache_file, "rb") as f:
        embeddings = pickle.load(f)
except FileNotFoundError:
    embeddings = transformer(smiles_list)
    with open(cache_file, "wb") as f:
        pickle.dump(embeddings, f)

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

*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:

grep -i "chembert" references/available_featurizers.md
grep -i "pharmacophore" references/available_featurizers.md

references/examples.md

Practical code examples for common scenarios:

Installation and quick start
Calculator and transformer examples
Pretrained model usage
Scikit-learn and PyTorch integration
Virtual screening workflows
QSAR model building
Similarity searching
Troubleshooting and best practices

When to load: Reference when implementing specific workflows, troubleshooting issues, or learning molfeat patterns.

Troubleshooting

Invalid Molecules

Enable error handling to skip invalid SMILES:

transformer = MoleculeTransformer(
    calc,
    ignore_errors=True,
    verbose=True
)

Memory Issues with Large Datasets

Process in chunks or use streaming approaches for datasets > 100K molecules.

Pretrained Model Dependencies

Some models require additional packages. Install specific extras:

uv pip install "molfeat[transformer]"  # For ChemBERTa/ChemGPT
uv pip install "molfeat[dgl]"          # For GIN models

Reproducibility

Save exact configurations and document versions:

transformer.to_state_yaml_file("config.yml")
import molfeat
print(f"molfeat version: {molfeat.__version__}")

Additional Resources

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

molfeat

Molfeat - Molecular Featurization Hub

Overview

When to Use This Skill

Installation

Core Concepts

1. Calculators (molfeat.calc)

2. Transformers (molfeat.trans)

3. Pretrained Transformers (molfeat.trans.pretrained)

Quick Start Workflow

Basic Featurization

Save and Load Configuration

Handle Errors Gracefully

Choosing the Right Featurizer

For Traditional Machine Learning (RF, SVM, XGBoost)

For Deep Learning

For Similarity Searching

For Pharmacophore-Based Approaches

Common Workflows

Building a QSAR Model

Virtual Screening Pipeline

Similarity Search

Scikit-learn Pipeline Integration

Comparing Multiple Featurizers

Discovering Available Featurizers

Advanced Features

Custom Preprocessing

Batch Processing Large Datasets

Caching Expensive Embeddings

Performance Tips

Common Featurizers Reference

Resources

references/api_reference.md

references/available_featurizers.md

references/examples.md

Troubleshooting

Invalid Molecules

Memory Issues with Large Datasets

Pretrained Model Dependencies

Reproducibility

Additional Resources

Related Skills

skill-creator

skill-creator

1password

himalaya

Molfeat - Molecular Featurization Hub

Overview

When to Use This Skill

Installation

Core Concepts

1. Calculators (molfeat.calc)

2. Transformers (molfeat.trans)

3. Pretrained Transformers (molfeat.trans.pretrained)

Quick Start Workflow

Basic Featurization

Save and Load Configuration

Handle Errors Gracefully

Choosing the Right Featurizer

For Traditional Machine Learning (RF, SVM, XGBoost)

For Deep Learning

For Similarity Searching

For Pharmacophore-Based Approaches

Common Workflows

Building a QSAR Model

Virtual Screening Pipeline

Similarity Search

Scikit-learn Pipeline Integration

Comparing Multiple Featurizers

Discovering Available Featurizers

Advanced Features

Custom Preprocessing

Batch Processing Large Datasets

Caching Expensive Embeddings

Performance Tips

Common Featurizers Reference

Resources

references/api_reference.md

references/available_featurizers.md

references/examples.md

1. Calculators (`molfeat.calc`)

2. Transformers (`molfeat.trans`)

3. Pretrained Transformers (`molfeat.trans.pretrained`)

1. Calculators (`molfeat.calc`)

2. Transformers (`molfeat.trans`)

3. Pretrained Transformers (`molfeat.trans.pretrained`)