Dataset Card for alchemy

Dataset Summary

The alchemy dataset is a molecular dataset, called Alchemy, which lists 12 quantum mechanical properties of 130,000+ organic molecules comprising up to 12 heavy atoms (C, N, O, S, F and Cl), sampled from the GDBMedChem database.

Supported Tasks and Leaderboards

alchemy should be used for organic quantum molecular property prediction, a regression task on 12 properties. The score used is MAE.

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	big
#graphs	202578
average #nodes	10.101387606810183
average #edges	20.877326870011206

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 is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.

Additional Information

Licensing Information

The dataset has been released under license mit.

Citation Information

@inproceedings{Morris+2020,
    title={TUDataset: A collection of benchmark datasets for learning with graphs},
    author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
    booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
    archivePrefix={arXiv},
    eprint={2007.08663},
    url={www.graphlearning.io},
    year={2020}
}

@article{DBLP:journals/corr/abs-1906-09427,
  author    = {Guangyong Chen and
               Pengfei Chen and
               Chang{-}Yu Hsieh and
               Chee{-}Kong Lee and
               Benben Liao and
               Renjie Liao and
               Weiwen Liu and
               Jiezhong Qiu and
               Qiming Sun and
               Jie Tang and
               Richard S. Zemel and
               Shengyu Zhang},
  title     = {Alchemy: {A} Quantum Chemistry Dataset for Benchmarking {AI} Models},
  journal   = {CoRR},
  volume    = {abs/1906.09427},
  year      = {2019},
  url       = {http://arxiv.org/abs/1906.09427},
  eprinttype = {arXiv},
  eprint    = {1906.09427},
  timestamp = {Mon, 11 Nov 2019 12:55:11 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-09427.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}