Source code for torchdrug.datasets.ppi_affinity

import os

from torch.utils import data as torch_data

from torchdrug import data, utils
from torchdrug.core import Registry as R

[docs]@R.register("datasets.PPIAffinity") @utils.copy_args(data.ProteinPairDataset.load_lmdbs, ignore=("sequence_field", "target_fields")) class PPIAffinity(data.ProteinPairDataset): r""" The binding affinity values measured by :math:`p_{K_d}` between two proteins. Statistics: - #Train: 2,127 - #Valid: 212 - #Test: 343 Parameters: path (str): the path to store the dataset verbose (int, optional): output verbose level **kwargs """ url = "" md5 = "d114907fd20c75820e41881f8901e9e4" splits = ["train", "valid", "test"] target_fields = ["interaction"] def __init__(self, path, verbose=1, **kwargs): path = os.path.expanduser(path) if not os.path.exists(path): os.makedirs(path) self.path = path zip_file =, path, md5=self.md5) data_path = utils.extract(zip_file) lmdb_files = [os.path.join(data_path, "ppi_affinity/ppi_affinity_%s.lmdb" % split) for split in self.splits] self.load_lmdbs(lmdb_files, sequence_field=["primary_1", "primary_2"], target_fields=self.target_fields, verbose=verbose, **kwargs) def split(self, keys=None): keys = keys or self.splits offset = 0 splits = [] for split_name, num_sample in zip(self.splits, self.num_samples): if split_name in keys: split = torch_data.Subset(self, range(offset, offset + num_sample)) splits.append(split) offset += num_sample return splits def get_item(self, index): if self.lazy: graph1 = data.Protein.from_sequence(self.sequences[index][0], **self.kwargs) graph2 = data.Protein.from_sequence(self.sequences[index][1], **self.kwargs) else: graph1 =[index][0] graph2 =[index][1] item = {"graph1": graph1, "graph2": graph2} item.update({k: v[index] for k, v in self.targets.items()}) if self.transform: item = self.transform(item) return item