import os
from rdkit import Chem
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
[docs]@R.register("datasets.BindingDB")
@utils.copy_args(data.ProteinLigandDataset.load_lmdbs, ignore=("sequence_field", "smiles_field", "target_fields"))
class BindingDB(data.ProteinLigandDataset):
"""
The BindingDB dataset with binding affinity indicating the interaction strength
between pairs of protein and ligand.
Statistics:
- #Train: 7,900
- #Valid: 878
- #Test: 5,230
Parameters:
path (str): the path to store the dataset
verbose (int, optional): output verbose level
**kwargs
"""
url = "https://miladeepgraphlearningproteindata.s3.us-east-2.amazonaws.com/peerdata/BindingDB_Kd.tar.gz"
md5 = "0b207cb962c4945f9003fc020b415a74"
splits = ["train", "valid", "random_test", "holdout_test"]
target_fields = ["affinity"]
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 = utils.download(self.url, path, md5=self.md5)
data_path = utils.extract(zip_file)
lmdb_files = [os.path.join(data_path, "BindingDB_Kd_%s.lmdb" % split) for split in self.splits]
self.load_lmdbs(lmdb_files, sequence_field="target", smiles_field="drug",
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], **self.kwargs)
mol = Chem.MolFromSmiles(self.smiles[index])
if not mol:
graph2 = None
else:
graph2 = data.Molecule.from_molecule(mol, **self.kwargs)
else:
graph1 = self.data[index][0]
graph2 = self.data[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