Multi-Omics and Artificial Intelligence-Guided Drug Repositioning: Prospects, Challenges, and Lessons Learned from COVID-19

被引:15
作者
Cong, Yi [1 ]
Endo, Toshinori [1 ,2 ]
机构
[1] Hokkaido Univ, Lab Informat Biol Informat Sci & Technol, Sapporo, Japan
[2] Hokkaido Univ, Fac Informat Sci & Technol, Lab Informat Biol, Res Grp Bioinformat, N14 W9, Sapporo 0600814, Japan
关键词
drug repositioning; Big Data; bioinformatics; machine learning; drug research and OMICS; COVID-19; GENE-EXPRESSION; CYTOKINE STORM; PREDICTION; NETWORKS;
D O I
10.1089/omi.2022.0068
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Drug repurposing is of interest for therapeutics innovation in many human diseases including coronavirus disease 2019 (COVID-19). Methodological innovations in drug repurposing are currently being empowered by convergence of omics systems science and digital transformation of life sciences. This expert review article offers a systematic summary of the application of artificial intelligence (AI), particularly machine learning (ML), to drug repurposing and classifies and introduces the common clustering, dimensionality reduction, and other methods. We highlight, as a present-day high-profile example, the involvement of AI/ML-based drug discovery in the COVID-19 pandemic and discuss the collection and sharing of diverse data types, and the possible futures awaiting drug repurposing in an era of AI/ML and digital technologies. The article provides new insights on convergence of multi-omics and AI-based drug repurposing. We conclude with reflections on the various pathways to expedite innovation in drug development through drug repurposing for prompt responses to the current COVID-19 pandemic and future ecological crises in the 21st century.
引用
收藏
页码:361 / 371
页数:11
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