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"""Implementation of MolGX conditional generators."""
import logging
import os
from typing import Any, Dict, List
from molgx.molgxsdk import MolgxSdk
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
[docs]class MolGXGenerator:
"""Interface for MolGX generator."""
[docs] def __init__(
self,
resources_path: str,
tag_name: str,
homo_energy_value: float = -0.25,
lumo_energy_value: float = 0.08,
use_linear_model: bool = True,
number_of_candidates: int = 2,
maximum_number_of_candidates: int = 3,
maximum_number_of_solutions: int = 3,
maximum_number_of_nodes: int = 50000,
beam_size: int = 2000,
without_estimate: bool = True,
use_specific_rings: bool = True,
use_fragment_const: bool = False,
) -> None:
"""Instantiate a MolGX generator.
Args:
resources_path: path to the resources for model loading.
tag_name: tag for the pretrained model.
homo_energy_value: target HOMO energy value. Defaults to -0.25.
lumo_energy_value: target LUMO energy value. Defaults to 0.08.
use_linear_model: linear model usage. Defaults to True.
number_of_candidates: number of candidates to consider. Defaults to 2.
maximum_number_of_candidates: maximum number of candidates to consider. Defaults to 3.
maximum_number_of_solutions: maximum number of solutions. Defaults to 3.
maximum_number_of_nodes: maximum number of nodes in the graph exploration. Defaults to 50000.
beam_size: size of the beam during search. Defaults to 2000.
without_estimate: disable estimates. Defaults to True.
use_specific_rings: flag to indicate whether specific rings are used. Defaults to True.
use_fragment_const: using constant fragments. Defaults to False.
Raises:
RuntimeError: in the case extras are disabled.
"""
# loading artifacts
self.resources_path = resources_path
self.tag_name = tag_name
self.amd = self.load_molgx(self.resources_path, self.tag_name)
self.molecules_data, self.target_property = self.amd.LoadPickle()
# algorithm parameters
self._homo_energy_value = homo_energy_value
self._lumo_energy_value = lumo_energy_value
self._use_linear_model = use_linear_model
self._number_of_candidates = number_of_candidates
self._maximum_number_of_candidates = maximum_number_of_candidates
self._maximum_number_of_solutions = maximum_number_of_solutions
self._maximum_number_of_nodes = maximum_number_of_nodes
self._beam_size = beam_size
self._without_estimate = without_estimate
self._use_specific_rings = use_specific_rings
self._use_fragment_const = use_fragment_const
self._parameters = self._create_parameters_dictionary()
[docs] @staticmethod
def load_molgx(resource_path: str, tag_name: str) -> MolgxSdk:
"""Load MolGX model.
Args:
resource_path: path to the resources for model loading.
tag_name: tag for the pretrained model.
Returns:
MolGX model SDK.
"""
return MolgxSdk(
pickle_filepath=os.path.join(resource_path, tag_name),
)
[docs] def _create_parameters_dictionary(self) -> Dict[str, Any]:
"""Create parameters dictionary.
Returns:
the parameters to run MolGX.
"""
self.target_property["homo"] = (-10, 10)
self.target_property["lumo"] = (-10, 10)
parameters: Dict[str, Any] = {}
parameters["target_property"] = self.target_property
parameters["use_linear_model"] = self.use_linear_model
parameters["num_candidate"] = self.number_of_candidates
parameters["max_candidate"] = self.maximum_number_of_candidates
parameters["max_solution"] = self.maximum_number_of_solutions
parameters["max_node"] = self.maximum_number_of_nodes
parameters["beam_size"] = self.beam_size
parameters["without_estimate"] = self.without_estimate
parameters["use_specific_rings"] = self.use_specific_rings
parameters["use_fragment_const"] = self.use_fragment_const
return parameters
@property
def homo_energy_value(self) -> float:
return self._homo_energy_value
@homo_energy_value.setter
def homo_energy_value(self, value: float) -> None:
self._homo_energy_value = value
self.parameters = self._create_parameters_dictionary()
@property
def lumo_energy_value(self) -> float:
return self._lumo_energy_value
@lumo_energy_value.setter
def lumo_energy_value(self, value: float) -> None:
self._lumo_energy_value = value
self.parameters = self._create_parameters_dictionary()
@property
def use_linear_model(self) -> bool:
return self._use_linear_model
@use_linear_model.setter
def use_linear_model(self, value: bool) -> None:
self._use_linear_model = value
self.parameters = self._create_parameters_dictionary()
@property
def number_of_candidates(self) -> int:
return self._number_of_candidates
@number_of_candidates.setter
def number_of_candidates(self, value: int) -> None:
self._number_of_candidates = value
self.parameters = self._create_parameters_dictionary()
@property
def maximum_number_of_candidates(self) -> int:
return self._maximum_number_of_candidates
@maximum_number_of_candidates.setter
def maximum_number_of_candidates(self, value: int) -> None:
self._maximum_number_of_candidates = value
self.parameters = self._create_parameters_dictionary()
@property
def maximum_number_of_solutions(self) -> int:
return self._maximum_number_of_solutions
@maximum_number_of_solutions.setter
def maximum_number_of_solutions(self, value: int) -> None:
self._maximum_number_of_solutions = value
self.parameters = self._create_parameters_dictionary()
@property
def maximum_number_of_nodes(self) -> int:
return self._maximum_number_of_nodes
@maximum_number_of_nodes.setter
def maximum_number_of_nodes(self, value: int) -> None:
self._maximum_number_of_nodes = value
self.parameters = self._create_parameters_dictionary()
@property
def beam_size(self) -> int:
return self._beam_size
@beam_size.setter
def beam_size(self, value: int) -> None:
self._beam_size = value
self.parameters = self._create_parameters_dictionary()
@property
def without_estimate(self) -> bool:
return self._without_estimate
@without_estimate.setter
def without_estimate(self, value: bool) -> None:
self._without_estimate = value
self.parameters = self._create_parameters_dictionary()
@property
def use_specific_rings(self) -> bool:
return self._use_specific_rings
@use_specific_rings.setter
def use_specific_rings(self, value: bool) -> None:
self._use_specific_rings = value
self.parameters = self._create_parameters_dictionary()
@property
def use_fragment_const(self) -> bool:
return self._use_fragment_const
@use_fragment_const.setter
def use_fragment_const(self, value: bool) -> None:
self._use_fragment_const = value
self.parameters = self._create_parameters_dictionary()
@property
def parameters(self) -> Dict[str, Any]:
return self._parameters
@parameters.setter
def parameters(self, value: Dict[str, Any]) -> None:
parameters = self._create_parameters_dictionary()
parameters.update(value)
self._parameters = parameters
[docs] def generate(self) -> List[str]:
"""Sample random molecules.
Returns:
sampled molecule (SMILES).
"""
# generate molecules
logger.info(f"running MolGX with the following parameters: {self.parameters}")
print(self.parameters)
molecules_df = self.amd.GenMols(self.molecules_data, self.parameters)
logger.info("MolGX run completed")
return molecules_df["SMILES"].tolist()