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README.md

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----
-license: unknown
----
-
-# 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.

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