Brizuela Carlos A, Liu Gary, Stokes Jonathan M, de la Fuente-Nunez Cesar
Department of Computer Science, CICESE Research Center, Ensenada, Mexico.
Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.
Microb Biotechnol. 2025 Jan;18(1):e70072. doi: 10.1111/1751-7915.70072.
Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. AI approaches have recently revolutionised this field by accelerating the discovery of new peptides with anti-infective activity, particularly in preclinical mouse models. Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure-guided AMP discovery and design. This review aims to fill that gap by providing a comprehensive overview of the latest advancements, challenges and opportunities in using AI methods, with a particular emphasis on LLMs, GNNs and structure-guided design. We discuss the limitations of current approaches and highlight the most relevant topics to address in the coming years for AMP discovery and design.
抗菌肽（AMPs）是对抗多重耐药病原体的有前景的候选物。然而，广泛的湿实验室筛选成本高昂，这使得用于鉴定和设计AMPs的人工智能方法变得越来越重要，机器学习（ML）技术在其中发挥着关键作用。人工智能方法最近通过加速发现具有抗感染活性的新肽，彻底改变了这一领域，特别是在临床前小鼠模型中。最初，经典的机器学习方法主导了该领域，但最近已经转向深度学习（DL）模型。尽管做出了重大贡献，但现有综述尚未全面探讨大语言模型（LLMs）、图神经网络（GNNs）以及结构导向的AMPs发现与设计的潜力。本综述旨在填补这一空白，全面概述使用人工智能方法的最新进展、挑战和机遇，特别强调大语言模型、图神经网络和结构导向设计。我们讨论了当前方法的局限性，并突出了未来几年AMPs发现与设计中最相关的需要解决的主题。
