#
# 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.
#
"""Implementation of the zero-shot classifier."""
import json
import logging
import os
from typing import Any, List, Optional, Union
import torch
from paccmann_predictor.models import MODEL_FACTORY
from pytoda.proteins.protein_language import ProteinLanguage
from pytoda.smiles.smiles_language import SMILESLanguage
from pytoda.transforms import LeftPadding, ToTensor
from ....frameworks.torch import device_claim
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
[docs]class MCAPredictor:
"""Base implementation of an MCAPredictor."""
[docs] def predict(self) -> Any:
"""Get prediction.
Returns:
predicted affinity
"""
raise NotImplementedError("No prediction implemented for base MCAPredictor")
[docs] def predict_values(self) -> Any:
"""Get prediction for algorithm sample method.
Returns:
predicted values as list.
"""
raise NotImplementedError(
"No values prediction implemented for base MCAPredictor"
)
[docs]class BimodalMCAAffinityPredictor(MCAPredictor):
"""Bimodal MCA (Multiscale Convolutional Attention) affinity prediction model.
For details see: https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.9b00520
and https://iopscience.iop.org/article/10.1088/2632-2153/abe808.
"""
[docs] def __init__(
self,
resources_path: str,
protein_targets: List[str],
ligands: List[str],
confidence: bool,
device: Optional[Union[torch.device, str]] = None,
):
"""Initialize BimodalMCAAffinityPredictor.
Args:
resources_path: path where to load model weights and cofiguration.
protein_targets: list of protein targets as AA sequences.
ligands: list of ligands in SMILES format.
confidence: whether the confidence for the prediction should be returned.
device: device where the inference
is running either as a dedicated class or a string. If not provided is inferred.
"""
self.device = device_claim(device)
self.resources_path = resources_path
self.protein_targets = protein_targets
self.ligands = ligands
self.confidence = confidence
# setting affinity predictor parameters
with open(os.path.join(resources_path, "mca_model_params.json")) as f:
self.predictor_params = json.load(f)
self.affinity_predictor = MODEL_FACTORY["bimodal_mca"](self.predictor_params)
self.affinity_predictor.load(
os.path.join(resources_path, "mca_weights.pt"),
map_location=self.device,
)
affinity_protein_language = ProteinLanguage.load(
os.path.join(resources_path, "protein_language.pkl")
)
affinity_smiles_language = SMILESLanguage.load(
os.path.join(resources_path, "smiles_language.pkl")
)
self.affinity_predictor._associate_language(affinity_smiles_language)
self.affinity_predictor._associate_language(affinity_protein_language)
self.affinity_predictor.eval()
self.pad_smiles_predictor = LeftPadding(
self.affinity_predictor.smiles_padding_length,
self.affinity_predictor.smiles_language.padding_index,
)
self.pad_protein_predictor = LeftPadding(
self.affinity_predictor.protein_padding_length,
self.affinity_predictor.protein_language.padding_index,
)
self.to_tensor = ToTensor()
[docs] def predict(self) -> Any:
"""Get predicted affinity.
Returns:
predicted affinity.
"""
# prepare ligand representation
ligand_tensor = torch.cat(
[
torch.unsqueeze(
self.to_tensor(
self.pad_smiles_predictor(
self.affinity_predictor.smiles_language.smiles_to_token_indexes(
ligand_smiles
)
)
),
0,
)
for ligand_smiles in self.ligands
],
dim=0,
)
# prepare target protein representation
target_tensor = torch.cat(
[
torch.unsqueeze(
self.to_tensor(
self.pad_protein_predictor(
self.affinity_predictor.protein_language.sequence_to_token_indexes(
protein_target
)
)
),
0,
)
for protein_target in self.protein_targets
],
dim=0,
)
with torch.no_grad():
predictions, predictions_dict = self.affinity_predictor(
ligand_tensor,
target_tensor,
confidence=self.confidence,
)
return predictions, predictions_dict
[docs] def predict_values(self) -> List[float]:
"""Get prediction for algorithm sample method.
Returns:
predicted values as list.
"""
predictions, _ = self.predict()
return list(predictions[:, 0])