ZINC / README.md
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Dataset Card for ZINC

Table of Contents

Dataset Description

Dataset Summary

The ZINC dataset is a "curated collection of commercially available chemical compounds prepared especially for virtual screening" (Wikipedia).

Supported Tasks and Leaderboards

ZINC should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE.

The associated leaderboard is here: Papers with code leaderboard.

External Use

PyGeometric

To load in PyGeometric, do the following:

from datasets import load_dataset

from torch_geometric.data import Data
from torch_geometric.loader import DataLoader

dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)

Dataset Structure

Data Properties

property value
scale big
#graphs 220011
average #nodes 23.15
average #edges 49.81

Data Fields

Each row of a given file is a graph, with:

  • node_feat (list: #nodes x #node-features): nodes
  • edge_index (list: 2 x #edges): pairs of nodes constituting edges
  • edge_attr (list: #edges x #edge-features): for the aforementioned edges, contains their features
  • y (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
  • num_nodes (int): number of nodes of the graph

Data Splits

This data comes from the PyGeometric version of the dataset, and follows the provided data splits. This information can be found back using

from torch_geometric.datasets import ZINC

dataset = ZINC(root = '', split='train') # valid, test

Additional Information

Licensing Information

The dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset.

Citation Information

@article{doi:10.1021/acs.jcim.5b00559,
  author = {Sterling, Teague and Irwin, John J.},
  title = {ZINC 15 – Ligand Discovery for Everyone},
  journal = {Journal of Chemical Information and Modeling},
  volume = {55},
  number = {11},
  pages = {2324-2337},
  year = {2015},
  doi = {10.1021/acs.jcim.5b00559},
      note ={PMID: 26479676},
  
  URL = { 
          https://doi.org/10.1021/acs.jcim.5b00559
      
  },
  eprint = { 
          https://doi.org/10.1021/acs.jcim.5b00559
      
  }
  
}

Contributions

Thanks to @clefourrier for adding this dataset.