A Unified View of Relational Deep Learning for Drug Pair Scoring
This work provides a foundational overview for researchers in computational drug discovery, but it is incremental as it synthesizes existing models rather than introducing new methods.
The paper presents a unified theoretical framework for relational machine learning models applied to drug pair scoring tasks, such as polypharmacy side effect identification and drug-drug interaction prediction, without reporting specific experimental results or performance numbers.
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.