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Review
. 2022 Apr 19:20:2112-2123.
doi: 10.1016/j.csbj.2022.04.021. eCollection 2022.

On the road to explainable AI in drug-drug interactions prediction: A systematic review

Thanh Hoa Vo  1 , Ngan Thi Kim Nguyen  2 , Quang Hien Kha  3 , Nguyen Quoc Khanh Le  4   5   6
Affiliations
Review

On the road to explainable AI in drug-drug interactions prediction: A systematic review

Thanh Hoa Vo et al. Comput Struct Biotechnol J. .

Abstract

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.

Keywords: Chemical structures; Deep learning; Drug-drug interaction; Explainable artificial intelligence; Machine learning; Natural language processing.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
PRISMA diagram showing our literature strategy search.
Fig. 2
Fig. 2
Overall workflow of traditional ML and DL for DDIs prediction.
Fig. 3
Fig. 3
Evolution of DDI prediction models separated by different input data and algorithms.
See this image and copyright information in PMC

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