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--- |
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license: unknown |
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task_categories: |
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- graph-ml |
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--- |
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# Dataset Card for MUTAG |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [External Use](#external-use) |
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- [PyGeometric](#pygeometric) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Properties](#data-properties) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **[Homepage](https://pubs.acs.org/doi/abs/10.1021/jm00106a046)** |
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- **[Repository](https://www.chrsmrrs.com/graphkerneldatasets/MUTAG.zip):**: |
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- **Paper:**: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity (see citation) |
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- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-mutag) |
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### Dataset Summary |
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The `MUTAG` dataset is 'a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium'. |
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### Supported Tasks and Leaderboards |
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`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. |
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## External Use |
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### PyGeometric |
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To load in PyGeometric, do the following: |
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```python |
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from datasets import load_dataset |
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from torch_geometric.data import Data |
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from torch_geometric.loader import DataLoader |
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dataset_hf = load_dataset("graphs-datasets/<mydataset>") |
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# For the train set (replace by valid or test as needed) |
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dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] |
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dataset_pg = DataLoader(dataset_pg_list) |
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``` |
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## Dataset Structure |
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### Data Properties |
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| property | value | |
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|---|---| |
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| scale | small | |
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| #graphs | 187 | |
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| average #nodes | 18.03 | |
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| average #edges | 39.80 | |
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### Data Fields |
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Each row of a given file is a graph, with: |
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- `node_feat` (list: #nodes x #node-features): nodes |
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- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges |
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- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features |
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- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one) |
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- `num_nodes` (int): number of nodes of the graph |
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### Data Splits |
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This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits. |
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This information can be found back using |
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```python |
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from torch_geometric.datasets import TUDataset |
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cur_dataset = TUDataset(root="../dataset/loaded/", |
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name="MUTAG") |
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``` |
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## Additional Information |
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### Licensing Information |
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The dataset has been released under unknown license, please open an issue if you have information. |
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### Citation Information |
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``` |
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@article{doi:10.1021/jm00106a046, |
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author = {Debnath, Asim Kumar and Lopez de Compadre, Rosa L. and Debnath, Gargi and Shusterman, Alan J. and Hansch, Corwin}, |
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title = {Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity}, |
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journal = {Journal of Medicinal Chemistry}, |
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volume = {34}, |
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number = {2}, |
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pages = {786-797}, |
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year = {1991}, |
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doi = {10.1021/jm00106a046}, |
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URL = { |
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https://doi.org/10.1021/jm00106a046 |
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}, |
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eprint = { |
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https://doi.org/10.1021/jm00106a046 |
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} |
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} |
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``` |
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### Contributions |
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Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. |
