Breakthroughs in AI and multi-omics for cancer drug discovery: A review.

Autor: Fatima I; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Rehman A; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China. Electronic address: abdurrehman@nwafu.edu.cn., Ding Y; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Wang P; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Meng Y; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Rehman HU; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Warraich DA; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Wang Z; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Feng L; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China., Liao M; Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China. Electronic address: liaomingzhi83@163.com.
Jazyk: angličtina
Zdroj: European journal of medicinal chemistry [Eur J Med Chem] 2024 Oct 04; Vol. 280, pp. 116925. Date of Electronic Publication: 2024 Oct 04.
DOI: 10.1016/j.ejmech.2024.116925
Abstrakt: Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.
Competing Interests: Declaration of competing interest 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.
(Copyright © 2024 Elsevier Masson SAS. All rights reserved.)
Databáze: MEDLINE
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