Abstract：
An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more \"trivial\" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.
摘要：
近年来，人们注意到解决生命科学中人工智能（AI）使用问题的出版物数量呈指数增长。而新的建模技术不断被报道。这些方法的潜力是巨大的-从理解基本的细胞过程到发现新药和突破性疗法。蛋白质-蛋白质相互作用的计算研究，对于理解生物系统的运作至关重要，在这个领域也不例外。然而,尽管技术的快速发展和开发新方法的进展，许多方面仍然有挑战性要解决，比如预测蛋白质的构象变化，或更多的“琐碎”问题，如大量的高质量数据。因此,本章重点介绍了研究蛋白质-蛋白质相互作用的各种人工智能方法，然后描述用于此目的的最新算法和程序。然而，考虑到计算科学这一热门领域的相当大的发展速度，当你读到这一章的时候,所描述的算法的发展，或者新的(和更好的)产品的出现应该不足为奇。
