人工智能和机器学习在药物再利用中的应用。

3区 生物学 Q2 Biochemistry, Genetics and Molecular Biology
Sudhir K Ghandikota, Anil G Jegga
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引用次数: 0

摘要

药物再利用的目的是利用以前批准用于特定疾病适应症的药物,将其应用于另一种疾病。它可以被视为一种更快、更具成本效益的药物发现方法,也是实现精准医疗的有力工具。此外,在疾病生物学信息有限的情况下,药物再利用还可用于确定罕见疾病和表型疾病的候选疗法。机器学习和人工智能(AI)方法通过整合和分析大规模生物医学数据,构建了有效的、数据驱动的再利用管道。最近的技术进步,尤其是异构网络挖掘和自然语言处理方面的进步,为药物再利用开辟了令人兴奋的新机遇和分析策略。在这篇综述中,我们首先介绍了再利用方法所面临的挑战,并重点介绍了一些成功案例,包括 COVID-19 大流行期间的成功案例。接下来,我们将根据所分析的生物医学输入数据类型和所涉及的计算算法,回顾文献中现有的一些计算框架。最后,我们概述了在生成式人工智能革命的推动下,药物再利用研究可能会出现的一些令人兴奋的新方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of artificial intelligence and machine learning in drug repurposing.

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

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来源期刊
CiteScore
5.00
自引率
0.00%
发文量
110
审稿时长
4-8 weeks
期刊介绍: Progress in Molecular Biology and Translational Science (PMBTS) provides in-depth reviews on topics of exceptional scientific importance. If today you read an Article or Letter in Nature or a Research Article or Report in Science reporting findings of exceptional importance, you likely will find comprehensive coverage of that research area in a future PMBTS volume.
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