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A Unified View of Relational Deep Learning for Drug Pair Scoring

4 November 2021
Benedek Rozemberczki
Stephen Bonner
A. Nikolov
M. 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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