人工智能系统的神奇猎枪药物设计

José Teófilo Moreira-Filho , Meryck Felipe Brito da Silva , Joyce Villa Verde Bastos Borba , Arlindo Rodrigues Galvão Filho , Eugene N Muratov , Carolina Horta Andrade , Rodolpho de Campos Braga , Bruno Junior Neves
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引用次数: 1

摘要

设计神奇的霰弹枪化合物,即使用基于机器学习(ML)和深度学习(DL)方法的人工智能(AI)系统击中多个目标的化合物,具有彻底改变药物发现的巨大潜力。这种智能系统使计算机能够以低成本和高效率的方式创造新的化学结构并预测其多目标特性。人工智能应用于药物发现的大多数例子都是单靶点导向的,关于将该技术应用于发现多靶点药物或具有广谱作用的药物方面,仍然缺乏简明的信息。在这篇综述中,我们重点介绍了用于下一代多靶点药物自动化设计的人工智能系统的最新发展。我们讨论了经典的机器学习方法、尖端的生成模型和多任务深度神经网络如何帮助多靶点药物的从头设计和hit-to-lead优化。此外,我们还介绍了最先进的工作流程,并重点介绍了一些展示令人鼓舞的实验结果的研究,这些实验结果为新药物设计和多靶点药物发现铺平了道路。
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Artificial intelligence systems for the design of magic shotgun drugs

Designing magic shotgun compounds, i.e., compounds hitting multiple targets using artificial intelligence (AI) systems based on machine learning (ML) and deep learning (DL) approaches, has a huge potential to revolutionize drug discovery. Such intelligent systems enable computers to create new chemical structures and predict their multi-target properties at a low cost and in a time-efficient manner. Most examples of AI applied to drug discovery are single-target oriented and there is still a lack of concise information regarding the application of this technology for the discovery of multi-target drugs or drugs with broad-spectrum action. In this review, we focus on current developments in AI systems for the next generation of automated design of multi-target drugs. We discuss how classical ML methods, cutting-edge generative models, and multi-task deep neural networks can help de novo design and hit-to-lead optimization of multi-target drugs. Moreover, we present state-of-the-art workflows and highlight some studies demonstrating encouraging experimental results, which pave the way for de novo drug design and multi-target drug discovery.

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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
发文量
0
审稿时长
15 days
期刊最新文献
Multi-objective synthesis planning by means of Monte Carlo Tree search Enhancing uncertainty quantification in drug discovery with censored regression labels Conformal prediction-based machine learning in Cheminformatics: Current applications and new challenges LIDEB's Useful Decoys (LUDe): A freely available decoy-generation tool. Benchmarking and scope “Foundation models for research: A matter of trust?”
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