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README.md

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----
-license: unknown
----
-
-# Dataset Card for ZINC
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-- [External Use](#external-use)
-  - [PyGeometric](#pygeometric)
-- [Dataset Structure](#dataset-structure)
-  - [Data Properties](#data-properties)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Additional Information](#additional-information)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **[Homepage](https://zinc15.docking.org/)**
-- **[Repository](https://www.dropbox.com/s/feo9qle74kg48gy/molecules.zip?dl=1):**: 
-- **Paper:**: ZINC 15 – Ligand Discovery for Everyone (see citation) 
-- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/)
-
-### 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](https://paperswithcode.com/sota/graph-regression-on-zinc).
-
-## External Use
-### PyGeometric
-To load in PyGeometric, do the following:
-
-```python
-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
-```python
-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
-```bibtex
-@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](https://github.com/clefourrier) for adding this dataset.

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