Recent Deep Learning Applications to Structure-Based Drug Design.
Recent Deep Learning Applications to Structure-Based Drug Design.
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最近的深度学习在基于结构的药物设计中的应用。
DOI：
10.1007/978-1-0716-3441-7_13
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Kihara,Daisuke
中科院分区：
文献类型：
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作者：
Verburgt,Jacob;Jain,Anika;Kihara,Daisuke
识别和优化与蛋白质结合并调节蛋白质功能的小分子是药物开发早期阶段的关键一步。几十年来，这一过程得益于计算模型的使用，这些模型可以提供对分子结合亲和力和优化的见解。在过去的几年里，各种类型的深度学习模型在改善和提高传统计算方法的性能方面显示出了巨大的潜力。在本章中，我们提供了基于深度学习的最新发展及其在药物发现中的应用的概述。根据每种方法要解决的任务，我们将这些方法分为四个子类别。对于每个子类别，我们提供了方法的总体框架，并讨论了单独的方法。
Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning–based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.