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README.md

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+# Dataset Card for MUTAG
+
+## 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://pubs.acs.org/doi/abs/10.1021/jm00106a046)**
+- **[Repository](https://www.chrsmrrs.com/graphkerneldatasets/MUTAG.zip):**: 
+- **Paper:**: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity (see citation) 
+- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-mutag)
+
+### Dataset Summary
+
+The `MUTAG` dataset is 'a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium'. 
+
+### Supported Tasks and Leaderboards
+
+`MUTAG` should be used for molecular property prediction (aiming to predict whether molecules have a mutagenic effect on a given bacterium or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
+
+## 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 | small |
+| #graphs | 187 |
+| average #nodes | 18.03 |
+| average #edges | 39.80 |
+
+### 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 provided by OGB, and follows the provided data splits.
+This information can be found back using
+```python
+from torch_geometric.datasets import TUDataset
+
+cur_dataset = TUDataset(root="../dataset/loaded/", 
+                               name="MUTAG")
+ ```
+
+## Additional Information
+
+### Licensing Information
+The dataset has been released under unknown license, please open an issue if you have information.
+
+### Citation Information
+```
+@article{doi:10.1021/jm00106a046,
+  author = {Debnath, Asim Kumar and Lopez de Compadre, Rosa L. and Debnath, Gargi and Shusterman, Alan J. and Hansch, Corwin},
+  title = {Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity},
+  journal = {Journal of Medicinal Chemistry},
+  volume = {34},
+  number = {2},
+  pages = {786-797},
+  year = {1991},
+  doi = {10.1021/jm00106a046},
+  URL = { 
+          https://doi.org/10.1021/jm00106a046
+  },
+  eprint = { 
+          https://doi.org/10.1021/jm00106a046 
+  }
+  
+}
+```
+
+### Contributions
+
+Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset.