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"""Implementation of PaccMann^RL conditional generators."""
import logging
from typing import List
import torch
from rdkit import Chem
from ...conditional_generation.paccmann_rl.core import (
PaccMannRL,
PaccMannRLProteinBasedGenerator,
)
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
[docs]class PaccMannVaeDefaultGenerator:
"""
Molecular generator as implemented in https://doi.org/10.1016/j.isci.2021.102269
"""
[docs] def __init__(
self,
temperature: float = 1.4,
batch_size: int = 32,
algorithm_version: str = "v0",
generated_length: int = 100,
) -> None:
"""
Initialize the generator.
Args:
batch_size: batch size used for generation.
algorithm_version: algorithm version for the PaccMannRLProteinBasedGenerator.
NOTE: Only the decoder of that model is used here.
temperature: temperature for the sampling. Defaults to 1.4.
generated_length: maximum length of the generated molecules.
Defaults to 100.
"""
self.configuration = PaccMannRLProteinBasedGenerator(
algorithm_version=algorithm_version,
temperature=temperature, # type: ignore
generated_length=generated_length, # type: ignore
batch_size=batch_size, # type: ignore
)
self.batch_size = batch_size
self.algorithm = PaccMannRL(configuration=self.configuration, target="")
self.model = self.configuration.get_conditional_generator(
self.algorithm.local_artifacts
)
[docs] def generate(self) -> List[str]:
"""
Generate a given number of samples (molecules) from a given protein.
Args:
number_of_molecules: number of molecules to sample.
Returns:
list of SMILES generated.
"""
smiles: List = []
while len(smiles) < self.batch_size:
# Define latent code
latent = torch.randn(1, self.batch_size, self.model.encoder_latent_size)
# Bypass algorithm.sample by decoding SMILES directly from latent
generated_smiles = self.model.get_smiles_from_latent(latent)
_, valid_ids = self.model.validate_molecules(generated_smiles)
valid_ids = [
i
for i in valid_ids
if len(
Chem.DetectChemistryProblems(
Chem.MolFromSmiles(generated_smiles[i])
)
)
== 0
]
generated_molecules = list([generated_smiles[index] for index in valid_ids])
smiles.extend(generated_molecules)
return smiles