"""A module for the drug feature set."""
from collections import UserDict
from typing import Dict, Iterable, Mapping, Union
import torch
from torchdrug.data import Graph, Molecule, PackedGraph
__all__ = [
"DrugFeatureSet",
]
[docs]class DrugFeatureSet(UserDict, Mapping[str, Mapping[str, Union[torch.FloatTensor, Molecule]]]):
"""Drug feature set for compounds."""
[docs] @classmethod
def from_dict(cls, data: Dict[str, Dict]) -> "DrugFeatureSet":
"""Generate a drug feature set from a data dictionary."""
return cls(
{
key: {
"features": torch.FloatTensor(features["features"]).view(1, -1),
"molecule": Molecule.from_smiles(features["smiles"]),
}
for key, features in data.items()
}
)
[docs] def get_feature_matrix(self, drugs: Iterable[str]) -> torch.FloatTensor:
"""Get the drug feature matrix for a list of drugs.
:param drugs: A list of drug identifiers.
:returns: A matrix of drug features.
"""
return torch.cat([self.data[drug]["features"] for drug in drugs])
[docs] def get_molecules(self, drugs: Iterable[str]) -> PackedGraph:
"""Get the molecular structures.
:param drugs: A list of drug identifiers.
:returns: The molecules batched together for message passing.
"""
return Graph.pack([self.data[drug]["molecule"] for drug in drugs])