A Unified View of Relational Deep Learning for Drug Pair Scoring
Abstract
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction, and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets, and evaluation protocols. In addition, we emphasize possible high-impact applications and important future research directions in this domain.
Cite
Text
Rozemberczki et al. "A Unified View of Relational Deep Learning for Drug Pair Scoring." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/777Markdown
[Rozemberczki et al. "A Unified View of Relational Deep Learning for Drug Pair Scoring." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/rozemberczki2022ijcai-unified/) doi:10.24963/IJCAI.2022/777BibTeX
@inproceedings{rozemberczki2022ijcai-unified,
title = {{A Unified View of Relational Deep Learning for Drug Pair Scoring}},
author = {Rozemberczki, Benedek and Bonner, Stephen and Nikolov, Andriy and Ughetto, Michaël and Nilsson, Sebastian and Papa, Eliseo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5564-5571},
doi = {10.24963/IJCAI.2022/777},
url = {https://mlanthology.org/ijcai/2022/rozemberczki2022ijcai-unified/}
}