
# 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.
task_categories:graph-ml license:unknown region:us
{"license": "unknown", "task_categories": ["graph-ml"], "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}}
TAGS
#task_categories-graph-ml #license-unknown #region-us 

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:


Dataset Structure
-----------------


### Data Properties



### 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


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.


### Contributions


Thanks to @clefourrier for adding this dataset.



### 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:


Dataset Structure
----------------- ### Data Properties ### 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


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. ### Contributions


Thanks to @clefourrier for adding this dataset.
TAGS
#task_categories-graph-ml #license-unknown #region-us 
 ### 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:


Dataset Structure
----------------- ### Data Properties ### 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


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. ### Contributions


Thanks to @clefourrier for adding this dataset.