README.md

metadata

license: unknown
dataset_info:
  features:
    - name: node_feat
      sequence:
        sequence: int64
    - name: edge_index
      sequence:
        sequence: int64
    - name: edge_attr
      sequence:
        sequence: int64
    - name: 'y'
      sequence: float64
    - name: num_nodes
      dtype: int64
  splits:
    - name: train
      num_bytes: 376796456
      num_examples: 220011
    - name: test
      num_bytes: 8538528
      num_examples: 5000
    - name: validation
      num_bytes: 41819628
      num_examples: 24445
  download_size: 20636253
  dataset_size: 427154612
task_categories:
  - graph-ml

Dataset Card for ZINC

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
• External Use
  • PyGeometric
• Dataset Structure
  • Data Properties
  • Data Fields
  • Data Splits
• Additional Information
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage
• Repository::
• Paper:: ZINC 15 – Ligand Discovery for Everyone (see citation)
• Leaderboard:: Papers with code leaderboard

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.