README.md

metadata

license: unknown
task_categories:
  - graph-ml

Dataset Card for MUTAG

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:: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity (see citation)
• Leaderboard:: Papers with code leaderboard

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:

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

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 for adding this dataset.