A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Synergy, and Drug-Drug Interaction Prediction

• URL: http://arxiv.org/abs/2111.02916v2
• Date: Fri, 5 Nov 2021 15:38:52 GMT
• Title: A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Synergy, and Drug-Drug Interaction Prediction
• Authors: Benedek Rozemberczki and Stephen Bonner and Andriy Nikolov and Michael Ughetto and Sebastian Nilsson and Eliseo Papa
• Abstract summary: 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.
• Score: 6.214548392474976
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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