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arXiv · 2111.02916

A Unified View of Relational Deep Learning for Drug Pair Scoring

Benedek Rozemberczki·Stephen Bonner·Andriy Nikolov·Michael Ughetto·Sebastian Nilsson·Eliseo Papa
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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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Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov, Michael Ughetto, Sebastian Nilsson, Eliseo Papa. 2021-11-04. A Unified View of Relational Deep Learning for Drug Pair Scoring. https://arxiv.org/abs/2111.02916

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