#
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
"""Prediction algorithms based on PaccMann"""
import logging
from dataclasses import field
from typing import Any, ClassVar, List, Optional, TypeVar
from ...core import AlgorithmConfiguration, GeneratorAlgorithm, Untargeted
from ...registry import ApplicationsRegistry
from .implementation import BimodalMCAAffinityPredictor, MCAPredictor
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
T = TypeVar("T", bound=Any)
S = TypeVar("S", bound=Any)
[docs]class PaccMann(GeneratorAlgorithm[S, T]):
"""PaccMann predictor."""
[docs] def __init__(
self,
configuration: AlgorithmConfiguration[S, T],
target: Optional[T] = None,
):
"""Instantiate PaccMann for prediction.
Args:
configuration: domain and application
specification defining parameters, types and validations.
target: a target for which to generate items.
Example:
An example for predicting affinity for a given ligand and target protein pair::
config = AffinityPredictor(
protein_targets=[
"MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT",
"MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT",
],
ligands=[
"CONN=COc1cc2ccccc2c11Occncc(Cl)c1N(O)O",
"ClCCC(O1)C(C(N=C1C(=O)Nc1cccc(F)c1F)SO)C",
]
)
algorithm = PaccMann(configuration=config)
items = list(algorithm.sample(1))
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 perform the prediction via PaccMann's generator.
Args:
configuration: helps to set up specific application of PaccMann.
target: context or condition for the generation.
Returns:
callable with target predicting properties using PaccMann.
"""
logger.info("ensure artifacts for the application are present.")
self.local_artifacts = configuration.ensure_artifacts()
implementation: MCAPredictor = configuration.get_conditional_generator( # type: ignore
self.local_artifacts
)
return implementation.predict_values
[docs]@ApplicationsRegistry.register_algorithm_application(PaccMann)
class AffinityPredictor(AlgorithmConfiguration[str, str]):
"""Configuration to predict affinity for a given ligand/protrin target pair."""
algorithm_type: ClassVar[str] = "prediction"
domain: ClassVar[str] = "materials"
algorithm_version: str = "v0"
protein_targets: List[str] = field(
default_factory=list,
metadata=dict(description="List of protein targets as AA sequences."),
)
ligands: List[str] = field(
default_factory=list,
metadata=dict(description="List of ligands in SMILES format."),
)
confidence: bool = field(
default=False,
metadata=dict(
description="Whether the confidence for the prediction should be returned."
),
)
[docs] def get_conditional_generator(
self, resources_path: str
) -> BimodalMCAAffinityPredictor:
"""Instantiate the actual predictor implementation.
Args:
resources_path: local path to model files.
Returns:
instance with :meth:`gt4sd.algorithms.prediction.affinity._predicto.implementation.BimodalMCAAffinityPredictor.predict` method for predicting affinity.
"""
return BimodalMCAAffinityPredictor(
resources_path=resources_path,
protein_targets=self.protein_targets,
ligands=self.ligands,
confidence=self.confidence,
)