
### dataset description
We downloaded ZINC dataset from [here](https://zinc15.docking.org/) and canonicalized it.
We used the following function to canonicalize the data and removed some SMILES that cannot be read by RDKit.
    
```python:
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.
language_creators:expert-generated multilinguality:monolingual size_categories:10M<n<100M source_datasets:original license:apache-2.0 ZINC chemical SMILES region:us
{"annotations_creators": [], "language_creators": ["expert-generated"], "language": [], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": [], "task_ids": [], "pretty_name": "canonicalized ZINC", "tags": ["ZINC", "chemical", "SMILES"]}
TAGS
#language_creators-expert-generated #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #license-apache-2.0 #ZINC #chemical #SMILES #region-us 


### 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.
    


We randomly split the preprocessed data into train and validation. The ratio is 9 : 1.
### 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.
    


We randomly split the preprocessed data into train and validation. The ratio is 9 : 1.
TAGS
#language_creators-expert-generated #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #license-apache-2.0 #ZINC #chemical #SMILES #region-us 
 ### 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.
    


We randomly split the preprocessed data into train and validation. The ratio is 9 : 1.
passage: TAGS
#language_creators-expert-generated #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #license-apache-2.0 #ZINC #chemical #SMILES #region-us 
### 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.
    


We randomly split the preprocessed data into train and validation. The ratio is 9 : 1.