A Unified View of Relational Deep Learning for Drug Pair Scoring

A Unified View of Relational Deep Learning for Drug Pair Scoring

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

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Survey Track. Pages 5564-5571. https://doi.org/10.24963/ijcai.2022/777

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.
Keywords:
Survey Track: -
Survey Track: Machine Learning
Survey Track: Data Mining
Survey Track: Knowledge Representation and Reasoning