---
name: molfeat
description: "สกิลสำหรับ Molfeat ใช้เมื่อต้องการแนวทาง ขั้นตอน Template หรือคำแนะนำเชิงปฏิบัติสำหรับงานวิเคราะห์ข้อมูล วิจัย AI ฐานข้อมูล วิทยาศาสตร์ข้อมูล และรายงานเชิงลึก ในหัวข้อนี้"
---

# Molfeat

## 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

```bash
uv pip install molfeat

# Molfeat
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:**
```python
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:**
```python
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:**
```python
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

```python
import datamol as dm
from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer

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

# Molfeat
calc = FPCalculator("ecfp", radius=3)
transformer = MoleculeTransformer(calc, n_jobs=-1)

# Molfeat
features = transformer(smiles)
print(f"Shape: {features.shape}")  # (4, 2048)
```

### Save and Load Configuration

```python
# Molfeat
transformer.to_state_yaml_file("featurizer_config.yML")

# Molfeat
loaded = MoleculeTransformer.from_state_yaml_file("featurizer_config.yML")
```

### Handle Errors Gracefully

```python
# Molfeat
transformer = MoleculeTransformer(
    calc,
    n_jobs=-1,
    ignore_errors=True,  # Continue on failures
    verbose=True          # Log error details
)

features = transformer(smiles_with_errors)
# Molfeat
```

## Choosing the Right Featurizer

### For Traditional Machine Learning (RF, SVM, XGBoost)

**Start with fingerprints:**
```python
# Molfeat
FPCalculator("ecfp", radius=3, fpSize=2048)

# Molfeat
FPCalculator("maccs")

# Molfeat
FPCalculator("map4")
```

**For interpretable models:**
```python
# Molfeat
from molfeat.calc import RDKitDescriptors2D
RDKitDescriptors2D()

# Molfeat
from molfeat.calc import MordredDescriptors
MordredDescriptors()
```

**Combine multiple featurizers:**
```python
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:**
```python
# Molfeat
PretrainedMolTransformer("ChemBERTa-77M-MLM")

# Molfeat
PretrainedMolTransformer("ChemGPT-1.2B")
```

**Graph neural networks:**
```python
# Molfeat
PretrainedMolTransformer("gin-supervised-masking")
PretrainedMolTransformer("gin-supervised-infomax")

# Molfeat
PretrainedMolTransformer("Graphormer-pcqm4mv2")
```

### For Similarity Searching

```python
# Molfeat
FPCalculator("ecfp")

# Molfeat
FPCalculator("maccs")

# Molfeat
FPCalculator("map4")

# Molfeat
from molfeat.calc import USRDescriptors
USRDescriptors()
```

### For Pharmacophore-Based Approaches

```python
# Molfeat
FPCalculator("fcfp")

# Molfeat
from molfeat.calc import CATSCalculator
CATSCalculator(mode="2D")

# Molfeat
FPCalculator("gobbi2D")
```

## Common Workflows

### Building a QSAR Model

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

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

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

# Molfeat
transformer.to_state_yaml_file("production_featurizer.yML")
```

### Virtual Screening Pipeline

```python
from sklearn.ensemble import RandomForestClassifier

# Molfeat
transformer = MoleculeTransformer(FPCalculator("ecfp"), n_jobs=-1)
X_train = transformer(train_smiles)
clf = RandomForestClassifier(n_estimators=500)
clf.fit(X_train, train_labels)

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

# Molfeat
top_indices = predictions.argsort()[::-1][:1000]
top_hits = [screening_library[i] for i in top_indices]
```

### Similarity Search

```python
from sklearn.metrics.pairwise import cosine_similarity

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

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

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

### Scikit-learn Pipeline Integration

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

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

# Molfeat
pipeline.fit(smiles_train, y_train)
predictions = pipeline.predict(smiles_test)
```

### Comparing Multiple Featurizers

```python
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:

```python
from molfeat.store.modelstore import ModelStore

store = ModelStore()

# Molfeat
all_models = store.available_models
print(f"Total featurizers: {len(all_models)}")

# Molfeat
chemberta_models = store.search(name="ChemBERTa")
for model in chemberta_models:
    print(f"- {model.name}: {model.description}")

# Molfeat
model_card = store.search(name="ChemBERTa-77M-MLM")[0]
model_card.usage()  # Display usage examples

# Molfeat
transformer = store.load("ChemBERTa-77M-MLM")
```

## Advanced Features

### Custom Preprocessing

```python
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

```python
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

```python
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

1. **Use parallelization**: Set `n_jobs=-1` to utilize all CPU cores
2. **Batch processing**: Process multiple molecules at once instead of loops
3. **Choose appropriate featurizers**: Fingerprints are faster than deep learning models
4. **Cache pretrained models**: Leverage built-in caching for repeated use
5. **Use float32**: Set `dtype=np.float32` when precision allows
6. **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:
```bash
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:
```python
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:
```bash
uv pip install "molfeat[transformer]"  # For ChemBERTa/ChemGPT
uv pip install "molfeat[dgl]"          # For GIN models
```

### Reproducibility
Save exact configurations and document versions:
```python
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
