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人工智能在变革药物研发中的作用。

Role of artificial intelligence in revolutionizing drug discovery.

作者信息

Rehman Ashfaq Ur, Li Mingyu, Wu Binjian, Ali Yasir, Rasheed Salman, Shaheen Sana, Liu Xinyi, Luo Ray, Zhang Jian

机构信息

Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Departments of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA 92697, United States.

出版信息

Fundam Res. 2024 May 9;5(3):1273-1287. doi: 10.1016/j.fmre.2024.04.021. eCollection 2025 May.

DOI:10.1016/j.fmre.2024.04.021
PMID:40528990
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12167903/
Abstract

The application of artificial intelligence (AI) in medicine, particularly through machine learning (ML), marked a significant progression in drug discovery. AI acts as a powerful catalyst in narrowing the gap between disease understanding and the identification of potential therapeutic agents. This review provides an inclusive summary of the latest advancements in AI and its application in drug discovery. We examine the various stages of the drug discovery process, starting from disease identification and encompassing diagnosis, target identification, screening, and lead discovery. AI's capability to analyze extensive datasets and discern patterns is essential in these stages, enhancing predictions and efficiencies in disease identification, drug discovery, and clinical trial management. The role of AI in expediting drug development is emphasized, highlighting its potential to analyze vast data volumes, thus reducing the time and costs associated with new drug market introduction. The importance of data quality, algorithm training, and ethical considerations, especially in patient data handling during clinical trials, is addressed. By considering these factors, AI promises to transform drug development, offering significant benefits to patients and society.

摘要

人工智能(AI)在医学中的应用,特别是通过机器学习(ML),标志着药物发现领域取得了重大进展。人工智能在缩小疾病认知与潜在治疗药物识别之间的差距方面起到了强大的推动作用。本综述全面总结了人工智能的最新进展及其在药物发现中的应用。我们考察了药物发现过程的各个阶段,从疾病识别开始,涵盖诊断、靶点识别、筛选和先导化合物发现。人工智能分析大量数据集并识别模式的能力在这些阶段至关重要,可提高疾病识别、药物发现和临床试验管理中的预测能力和效率。强调了人工智能在加速药物开发中的作用,突出了其分析海量数据的潜力,从而减少与新药上市相关的时间和成本。还讨论了数据质量、算法训练和伦理考量的重要性,特别是在临床试验期间处理患者数据时。通过考虑这些因素,人工智能有望变革药物开发,为患者和社会带来巨大益处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/7a323c14522e/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/bd22f7ccaa15/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/86b1fb396d4d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/2f45e25e85d1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/7a323c14522e/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/bd22f7ccaa15/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/86b1fb396d4d/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/2f45e25e85d1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63eb/12167903/7a323c14522e/gr4.jpg

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