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README.md

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+# 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.