MolecularDiffusion.runmodes.generate.tasks_generate

Attributes

logger

Classes

GenerativeFactory

Module Contents

class MolecularDiffusion.runmodes.generate.tasks_generate.GenerativeFactory(task: MolecularDiffusion.modules.tasks.task.Task, task_type: str = 'unconditional', sampling_mode: str = 'ddpm', num_generate: int = 100, mol_size: List[int] = [0, 0], target_values: List[float] = [], property_names: List[str] = [], negative_target_values: List[float] = [], batch_size: int = 1, seed: int = 86, n_frames: int = 0, output_path: str = 'generated_mol', condition_configs={})

conditional_generation()

hybrid_guidance()

preprocess_ref_structure(device)

Load and preprocess a reference molecular structure from an XYZ file.

This function reads an XYZ file, encodes atomic features, normalizes coordinates and features, and returns a tensor combining positions and processed features.

Returns:

Tensor of shape (1, n_atoms, 3 + n_features + 1) containing:

[normalized_coords | normalized_onehot_features | normalized_charges].

Return type:

torch.Tensor

Raises:

• FileNotFoundError – If the reference structure file is not found.

• ValueError – If the processed reference structure is empty.

property_guidance()

property_prediction(xh: torch.Tensor, t: int)

run()

structural_guidance()

unconditional_generation()

batch_size = 1

condition_configs

mol_size = [0, 0]

n_frames = 0

negative_target_values = []

num_generate = 100

output_path = 'generated_mol'

property_names = []

sampling_mode = 'ddpm'

seed = 86

target_values = []

task

task_type = 'unconditional'

MolecularDiffusion.runmodes.generate.tasks_generate.logger