Attention is all you need: utilizing attention in AI-enabled drug discovery.

Autor:	Zhang Y; Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Liu C; Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China.; Key Laboratory of Molecular Oncology of Heilongjiang Province, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China., Liu M; Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China., Liu T; Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan., Lin H; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China., Huang CB; School of Computer Science and Technology, Aba Teachers University, Aba, China., Ning L; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.; School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China.
Jazyk:	angličtina
Zdroj:	Briefings in bioinformatics [Brief Bioinform] 2023 Nov 22; Vol. 25 (1).
DOI:	10.1093/bib/bbad467
Abstrakt:	Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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