Quick Start Example
# Generate protein sequences for target structures
from protein_design import StructuredTransformer, DiffusionModel
# Initialize model
model = StructuredTransformer(node_dim=128, num_heads=8)
diffusion = DiffusionModel(model)
# Generate sequences
sequences = diffusion.sample(target_structures, num_samples=10)
print(f'Generated {len(sequences)} novel protein sequences')
# Validate designs
validation_results = validate_designs(sequences, target_structures)
print(f'Success rate: {validation_results.success_rate:.2%}')
Model Architecture
class StructuredTransformer(nn.Module):
def __init__(self, node_dim=128, num_heads=8, num_layers=6):
super().__init__()
self.node_embedding = nn.Linear(20, node_dim) # Amino acid embedding
self.pos_embedding = nn.Linear(3, node_dim) # 3D position embedding
# Graph attention layers
self.attention_layers = nn.ModuleList([
GraphAttentionLayer(node_dim, num_heads)
for _ in range(num_layers)
])
# Decoder for sequence generation
self.decoder = AutoregressiveDecoder(node_dim, vocab_size=20)
def forward(self, structure_graph, target_sequence=None):
# Encode structure
node_features = self.encode_structure(structure_graph)
# Generate or predict sequence
if target_sequence is not None:
return self.decoder(node_features, target_sequence)
else:
return self.decoder.generate(node_features)
Training Pipeline
# Training configuration
config = TrainingConfig(
batch_size=32,
learning_rate=1e-4,
num_epochs=100,
diffusion_steps=1000,
noise_schedule='cosine'
)
# Initialize trainer
trainer = ProteinDesignTrainer(model, config)
# Train model
trainer.fit(
train_dataset=protein_dataset,
val_dataset=val_dataset,
callbacks=[
ModelCheckpoint(),
EarlyStopping(patience=10),
ValidationLogger()
]
)
# Evaluation
results = trainer.evaluate(test_dataset)
print(f"Test sequence recovery: {results['sequence_recovery']:.2%}")
print(f"Test success rate: {results['success_rate']:.2%}")
