molecule-benchmarks 0.1.1

A comprehensive benchmark suite for evaluating generative models for molecules

Molecule Benchmarks

A comprehensive benchmark suite for evaluating generative models for molecules. This package provides standardized metrics and evaluation protocols for assessing the quality of molecular generation models in drug discovery and cheminformatics.

Features

• Comprehensive Metrics: Validity, uniqueness, novelty, diversity, and similarity metrics
• Standard Benchmarks: Implements metrics from Moses, GuacaMol, and FCD papers
• Easy Integration: Simple interface for integrating with any generative model
• Multiple Datasets: Built-in support for QM9, Moses, and GuacaMol datasets
• Efficient Computation: Optimized for large-scale evaluation with multiprocessing support

Installation

pip install molecule-benchmarks

Quick Start

1. Implement Your Model

To use the benchmark suite, implement the MoleculeGenerationModel protocol:

from molecule_benchmarks.model import MoleculeGenerationModel

class MyGenerativeModel(MoleculeGenerationModel):
    def __init__(self, model_path):
        # Initialize your model here
        self.model = load_model(model_path)
    
    def generate_molecule_batch(self) -> list[str | None]:
        """Generate a batch of molecules as SMILES strings.
        
        Returns:
            List of SMILES strings. Return None for invalid molecules.
        """
        # Your generation logic here
        batch = self.model.generate(batch_size=100)
        return [self.convert_to_smiles(mol) for mol in batch]

2. Run Benchmarks

from molecule_benchmarks import Benchmarker, SmilesDataset

# Load a dataset
dataset = SmilesDataset.load_qm9_dataset(subset_size=10000)

# Initialize benchmarker
benchmarker = Benchmarker(
    dataset=dataset,
    num_samples_to_generate=10000,
    device="cpu"  # or "cuda" for GPU
)

# Initialize your model
model = MyGenerativeModel("path/to/model")

# Run benchmarks
results = benchmarker.benchmark(model)
print(results)

3. Analyze Results

The benchmark returns comprehensive metrics:

# Validity metrics
print(f"Valid molecules: {results['validity']['valid_fraction']:.3f}")
print(f"Valid & unique: {results['validity']['valid_and_unique_fraction']:.3f}")
print(f"Valid & unique & novel: {results['validity']['valid_and_unique_and_novel_fraction']:.3f}")

# Diversity and similarity metrics
print(f"Internal diversity: {results['moses']['IntDiv']:.3f}")
print(f"SNN score: {results['moses']['snn_score']:.3f}")

# Chemical property distribution similarity
print(f"KL divergence score: {results['kl_score']:.3f}")

# Fréchet ChemNet Distance
print(f"FCD score: {results['fcd']['fcd']:.3f}")

Complete Example

Here's a complete example using the built-in dummy model:

from molecule_benchmarks import Benchmarker, SmilesDataset
from molecule_benchmarks.model import DummyMoleculeGenerationModel

# Load dataset
print("Loading dataset...")
dataset = SmilesDataset.load_qm9_dataset(subset_size=1000)

# Create benchmarker
benchmarker = Benchmarker(
    dataset=dataset,
    num_samples_to_generate=100,
    device="cpu"
)

# Create a dummy model (replace with your model)
model = DummyMoleculeGenerationModel([
    "CCO",           # Ethanol
    "CC(=O)O",       # Acetic acid
    "c1ccccc1",      # Benzene
    "CC(C)O",        # Isopropanol
    "CCN",           # Ethylamine
    None,            # Invalid molecule
])

# Run benchmarks
print("Running benchmarks...")
results = benchmarker.benchmark(model)

# Print results
print("\n=== Validity Metrics ===")
print(f"Valid molecules: {results['validity']['valid_fraction']:.3f}")
print(f"Unique molecules: {results['validity']['unique_fraction']:.3f}")
print(f"Valid & unique: {results['validity']['valid_and_unique_fraction']:.3f}")
print(f"Novel molecules: {results['validity']['valid_and_unique_and_novel_fraction']:.3f}")

print("\n=== Moses Metrics ===")
print(f"Passing Moses filters: {results['moses']['fraction_passing_moses_filters']:.3f}")
print(f"SNN score: {results['moses']['snn_score']:.3f}")
print(f"Internal diversity (p=1): {results['moses']['IntDiv']:.3f}")
print(f"Internal diversity (p=2): {results['moses']['IntDiv2']:.3f}")

print("\n=== Distribution Metrics ===")
print(f"KL divergence score: {results['kl_score']:.3f}")
print(f"FCD score: {results['fcd']['fcd']:.3f}")
print(f"FCD (valid only): {results['fcd']['fcd_valid']:.3f}")

Supported Datasets

The package includes several built-in datasets:

from molecule_benchmarks import SmilesDataset

# QM9 dataset (small molecules)
dataset = SmilesDataset.load_qm9_dataset(subset_size=10000)

# Moses dataset (larger, drug-like molecules)
dataset = SmilesDataset.load_moses_dataset(fraction=0.1)

# GuacaMol dataset
dataset = SmilesDataset.load_guacamol_dataset(fraction=0.1)

# Custom dataset from files
dataset = SmilesDataset(
    train_smiles="path/to/train.txt",
    validation_smiles="path/to/valid.txt"
)

Metrics Explained

Validity Metrics

• Valid fraction: Percentage of generated molecules that are chemically valid
• Unique fraction: Percentage of generated molecules that are unique
• Novel fraction: Percentage of generated molecules not seen in training data

Moses Metrics

Based on the Moses paper:

• SNN score: Similarity to nearest neighbor in training set
• Internal diversity: Average pairwise Tanimoto distance within generated set
• Scaffold similarity: Similarity of molecular scaffolds to training set
• Fragment similarity: Similarity of molecular fragments to training set

Distribution Metrics

• KL divergence score: Measures similarity of molecular property distributions
• FCD score: Fréchet ChemNet Distance, measures distribution similarity in learned feature space

Advanced Usage

Custom Evaluation

# Custom number of samples and device
benchmarker = Benchmarker(
    dataset=dataset,
    num_samples_to_generate=50000,
    device="cuda"  # Use GPU for faster computation
)

# Run specific metric computations
results = benchmarker.benchmark(model)
validity_scores = benchmarker._compute_validity_scores(generated_smiles)
fcd_scores = benchmarker._compute_fcd_scores(generated_smiles)

Batch Processing

class BatchedModel(MoleculeGenerationModel):
    def generate_molecule_batch(self) -> list[str | None]:
        # Generate larger batches for efficiency
        return self.model.sample(batch_size=1000)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This benchmark suite implements and builds upon metrics from several important papers:

• Moses: A Benchmarking Platform for Molecular Generation Models
• GuacaMol: Benchmarking Models for De Novo Molecular Design
• Fréchet ChemNet Distance: A Metric for Generative Models for Molecules