"""
The original 250k ZINC dataset from the `ZINC database
<https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00559>`_ and the
`"Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
<https://arxiv.org/abs/1610.02415>`_ paper
"""
import os
import os.path as osp
import shutil
import torch
from torch_geometric.data import InMemoryDataset, download_url
from tqdm import tqdm
from GOOD.utils.data import from_smiles
[docs]class ZINC(InMemoryDataset):
r"""The ZINC dataset from the `ZINC database
<https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00559>`_ and the
`"Automatic Chemical Design Using a Data-Driven Continuous Representation
of Molecules" <https://arxiv.org/abs/1610.02415>`_ paper, containing about
250,000 molecular graphs with up to 38 heavy atoms.
The task is to regress a synthetic computed property dubbed as the
constrained solubility.
Args:
root (string): Root directory where the dataset should be saved.
subset (boolean, optional): If set to :obj:`True`, will only load a
subset of the dataset (12,000 molecular graphs), following the
`"Benchmarking Graph Neural Networks"
<https://arxiv.org/abs/2003.00982>`_ paper. (default: :obj:`False`)
split (string, optional): If :obj:`"train"`, loads the training
dataset.
If :obj:`"val"`, loads the validation dataset.
If :obj:`"test"`, loads the test dataset.
(default: :obj:`"train"`)
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
"""
url = 'https://raw.githubusercontent.com/aspuru-guzik-group/chemical_vae/master/models/zinc_properties/250k_rndm_zinc_drugs_clean_3.csv'
def __init__(self, root, name, transform=None,
pre_transform=None, pre_filter=None, subset=False):
self.name = 'zinc'
self.subset = subset
super().__init__(root, transform, pre_transform, pre_filter)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name, 'raw')
@property
def raw_file_names(self):
return ['zinc.csv']
@property
def processed_dir(self):
return osp.join(self.root, self.name, 'processed')
@property
def processed_file_names(self):
return ['data.pt']
[docs] def download(self):
shutil.rmtree(self.raw_dir)
path = download_url(self.url, osp.join(self.root, self.name))
# extract_zip(path, self.root)
os.mkdir(self.raw_dir)
os.rename(osp.join(self.root, self.name, '250k_rndm_zinc_drugs_clean_3.csv'), self.raw_paths[0])
[docs] def process(self):
data_list = []
import csv
with open(self.raw_paths[0], "r") as csv_fp:
csv_reader = csv.reader(csv_fp)
fields = {field: i for i, field in enumerate(next(csv_reader))}
for i, raw_data in tqdm(enumerate(csv_reader)):
smiles = raw_data[fields['smiles']]
data, mol = from_smiles(smiles)
cycles = mol.GetRingInfo().AtomRings()
num_cycles_le6 = 0
for cycle in cycles:
if cycle.__len__() >= 6:
num_cycles_le6 += 1
data.y = float(raw_data[fields['logP']]) - float(raw_data[fields['SAS']]) - float(num_cycles_le6)
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
if self.subset and i >= 1000:
break
torch.save(self.collate(data_list), self.processed_paths[0])