Datasets:
metadata
annotations_creators: []
language: []
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: canonicalized ZINC
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- ZINC
- chemical
- SMILES
task_categories: []
task_ids: []
dataset description
We downloaded ZINC dataset from here and canonicalized it. We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
from rdkit import Chem
def canonicalize(mol):
mol = Chem.MolToSmiles(Chem.MolFromSmiles(mol),True)
return mol
We randomly split the preprocessed data into train and validation. The ratio is 9 : 1.