A Unified View of Relational Deep Learning for Drug Pair Scoring

Benedek Rozemberczki; Stephen Bonner; Andriy Nikolov; Michaël Ughetto; Sebastian Nilsson; Eliseo Papa

IJCAI 2022poster18 citations

A Unified View of Relational Deep Learning for Drug Pair Scoring

Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov, Michaël Ughetto, Sebastian Nilsson, Eliseo Papa

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.

Survey Track: -Survey Track: Machine LearningSurvey Track: Data MiningSurvey Track: Knowledge Representation and Reasoning

BibTeX

@inproceedings{ijcai2022p777,
  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 = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {5564--5571},
  year      = {2022},
  month     = {7},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2022/777},
  url       = {https://doi.org/10.24963/ijcai.2022/777},
}