Artificial intelligence-driven prediction of multiple drug interactions. Issue 6 (27th September 2022)

• Record Type: Journal Article
• Authors: Chen, Siqi; Li, Tiancheng; Yang, Luna; Zhai, Fei; Jiang, Xiwei; Xiang, Rongwu; Ling, Guixia
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-09-27
• Subjects: artificial intelligence -- multiple drug interactions -- machine learning -- deep learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac427
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug–drug, drug–food (excipients) and drug–microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug–drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions. Graphical Abstract: