import os
from torchdrug import data, utils
from torchdrug.core import Registry as R
[docs]@R.register("datasets.MUV")
@utils.copy_args(data.MoleculeDataset.load_csv, ignore=("smiles_field", "target_fields"))
class MUV(data.MoleculeDataset):
"""
Subset of PubChem BioAssay by applying a refined nearest neighbor analysis.
Statistics:
- #Molecule: 93,087
- #Classification task: 17
Parameters:
path (str): path to store the dataset
verbose (int, optional): output verbose level
**kwargs
"""
url = "http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/muv.csv.gz"
md5 = "9c40bd41310991efd40f4d4868fa3ddf"
target_fields = ["MUV-466", "MUV-548", "MUV-600", "MUV-644", "MUV-652", "MUV-689", "MUV-692", "MUV-712", "MUV-713",
"MUV-733", "MUV-737", "MUV-810", "MUV-832", "MUV-846", "MUV-852", "MUV-858", "MUV-859"]
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)
csv_file = utils.extract(zip_file)
self.load_csv(csv_file, smiles_field="smiles", target_fields=self.target_fields,
verbose=verbose, **kwargs)