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"""MolGX Algorithm.
MolGX generation algorithm.
"""
import logging
from dataclasses import field
from typing import Any, ClassVar, Dict, Iterator, Optional, TypeVar
from ....domains.materials import SMILES, MoleculeFormat, validate_molecules
from ....exceptions import InvalidItem
from ...core import AlgorithmConfiguration, GeneratorAlgorithm, Untargeted
from ...registry import ApplicationsRegistry
from .implementation import MolGXGenerator
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
T = type(None)
S = TypeVar("S", bound=SMILES)
[docs]class MolGX(GeneratorAlgorithm[S, T]):
"""MolGX Algorithm."""
[docs] def __init__(
self,
configuration: AlgorithmConfiguration[S, T],
target: Optional[T] = None,
):
"""Instantiate MolGX ready to generate items.
Args:
configuration: domain and application
specification defining parameters, types and validations.
target: a target for which to generate items.
Example:
An example for generating small molecules (SMILES) with given HOMO and LUMO energies:
configuration = MolGXQM9Generator()
molgx = MolGX(configuration=configuration, target=target)
items = list(molgx.sample(10))
print(items)
"""
configuration = self.validate_configuration(configuration)
# TODO there might also be a validation/check on the target input
super().__init__(
configuration=configuration, # type:ignore
target=target, # type:ignore
)
[docs] def get_generator(
self,
configuration: AlgorithmConfiguration[S, T],
target: Optional[T],
) -> Untargeted:
"""Get the function to sample batches via the ConditionalGenerator.
Args:
configuration: helps to set up the application.
target: context or condition for the generation. Unused in the algorithm.
Returns:
callable generating a batch of items.
"""
logger.info("ensure artifacts for the application are present.")
self.local_artifacts = configuration.ensure_artifacts()
implementation: MolGXGenerator = configuration.get_conditional_generator( # type: ignore
self.local_artifacts
)
return implementation.generate
[docs] def validate_configuration(
self, configuration: AlgorithmConfiguration[S, T]
) -> AlgorithmConfiguration[S, T]:
# TODO raise InvalidAlgorithmConfiguration
assert isinstance(configuration, AlgorithmConfiguration)
return configuration
[docs] def sample(self, number_of_items: int = 100) -> Iterator[S]:
"""Generate a number of unique and valid items.
Args:
number_of_items: number of items to generate. Defaults to 100.
Yields:
the items.
"""
if hasattr(self.configuration, "maximum_number_of_solutions"):
maxiumum_number_of_molecules = int(
getattr(self.configuration, "maximum_number_of_solutions")
)
if number_of_items > maxiumum_number_of_molecules:
logger.warning(
f"current MolGX configuration can not support generation of {number_of_items} molecules..."
)
logger.warning(
f"to enable generation of {number_of_items} molecules, increase 'maximum_number_of_solutions' (currently set to {maxiumum_number_of_molecules})"
)
number_of_items = maxiumum_number_of_molecules
logger.warning(f"generating at most: {maxiumum_number_of_molecules}...")
return super().sample(number_of_items=number_of_items)
[docs]@ApplicationsRegistry.register_algorithm_application(MolGX)
class MolGXQM9Generator(AlgorithmConfiguration[SMILES, Any]):
"""Configuration to generate compounds with given HOMO and LUMO energies."""
algorithm_type: ClassVar[str] = "conditional_generation"
domain: ClassVar[str] = "materials"
algorithm_version: str = "v0"
homo_energy_value: float = field(
default=-0.25,
metadata=dict(description="Target HOMO energy value."),
)
lumo_energy_value: float = field(
default=0.08,
metadata=dict(description="Target LUMO energy value."),
)
use_linear_model: bool = field(
default=True,
metadata=dict(description="Linear model usage."),
)
number_of_candidates: int = field(
default=2,
metadata=dict(description="Number of candidates to consider."),
)
maximum_number_of_candidates: int = field(
default=5,
metadata=dict(description="Maximum number of candidates to consider."),
)
maximum_number_of_solutions: int = field(
default=10,
metadata=dict(description="Maximum number of solutions."),
)
maximum_number_of_nodes: int = field(
default=50000,
metadata=dict(description="Maximum number of nodes in the graph exploration."),
)
beam_size: int = field(
default=2000,
metadata=dict(description="Size of the beam during search."),
)
without_estimate: bool = field(
default=True,
metadata=dict(description="Disable estimates."),
)
use_specific_rings: bool = field(
default=True,
metadata=dict(description="Flag to indicate whether specific rings are used."),
)
use_fragment_const: bool = field(
default=False,
metadata=dict(description="Using constant fragments."),
)
[docs] def get_target_description(self) -> Optional[Dict[str, str]]:
"""Get description of the target for generation.
Returns:
target description, returns None in case no target is used.
"""
return None
[docs] def get_conditional_generator(self, resources_path: str) -> MolGXGenerator:
"""Instantiate the actual generator implementation.
Args:
resources_path: local path to model files.
Returns:
instance with :meth:`generate<gt4sd.algorithms.conditional_generation.molgx.implementation.MolGXGenerator.generate>` for generation.
"""
return MolGXGenerator(
resources_path=resources_path,
homo_energy_value=self.homo_energy_value,
lumo_energy_value=self.lumo_energy_value,
use_linear_model=self.use_linear_model,
number_of_candidates=self.number_of_candidates,
maximum_number_of_candidates=self.maximum_number_of_candidates,
maximum_number_of_solutions=self.maximum_number_of_solutions,
maximum_number_of_nodes=self.maximum_number_of_nodes,
beam_size=self.beam_size,
without_estimate=self.without_estimate,
use_specific_rings=self.use_specific_rings,
use_fragment_const=self.use_fragment_const,
tag_name="qm9_sample_pretrained_model.pickle",
)
[docs] def validate_item(self, item: str) -> SMILES:
"""Check that item is a valid SMILES.
Args:
item: a generated item that is possibly not valid.
Raises:
InvalidItem: in case the item can not be validated.
Returns:
the validated SMILES.
"""
(
molecules,
_,
) = validate_molecules([item], MoleculeFormat.smiles)
if molecules[0] is None:
raise InvalidItem(
title="InvalidSMILES",
detail=f'rdkit.Chem.MolFromSmiles returned None for "{item}"',
)
return SMILES(item)