Peptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anticancer peptides (ACPs) are prohibitively costly and time-consuming. Furthermore, the sequential increase in the protein sequence data via highthroughput sequencing makes it difficult to identify ACPs only through experimentation, which often involves months or years of speculation and failure. All these limitations could be overcome by applying machine learning (ML) approaches, which is a field of artificial intelligence that automates analytical model building for rapid and accurate outcome predictions. Recently, ML approaches hold great promise in the rapid discovery of ACPs, which could be witnessed by the growing number of MLbased anticancer prediction tools. In this review, we aim to provide a comprehensive view on the existing ML approaches for ACP predictions. Initially, we will briefly discuss the currently available ACP databases. This is followed by the main text, where state-of-the-art ML approaches working principles and their performances based on the ML algorithms are reviewed. Lastly, we discuss the limitations and future directions of the ML methods in the prediction of ACPs.

肽由于易于合成和修饰，增强的肿瘤渗透性和较低的全身毒性而可作为有前途的抗癌剂。但是，到目前为止，由于抗癌肽（ACP）的实验设计和合成费用过高且费时，因此仅获得了有限的成功。此外，通过高通量测序的蛋白质序列数据的顺序增加使得仅通过实验很难鉴定ACP，这通常需要数月或数年的推测和失败。所有这些限制都可以通过应用机器学习（ML）方法来克服，机器学习（ML）方法是人工智能领域，它可以自动构建分析模型以进行快速，准确的结果预测。最近，机器学习方法在快速发现ACP中具有广阔的前景，越来越多的基于ML的抗癌预测工具可以证明这一点。在这篇综述中，我们旨在提供有关ACP预测的现有ML方法的全面视图。最初，我们将简要讨论当前可用的ACP数据库。紧随其后的是正文，其中回顾了最新的机器学习方法的工作原理及其基于机器学习算法的性能。最后，我们讨论了ML方法在ACP预测中的局限性和未来发展方向。回顾了最新的机器学习方法的工作原理及其基于机器学习算法的性能。最后，我们讨论了ML方法在ACP预测中的局限性和未来发展方向。回顾了最先进的机器学习方法的工作原理及其基于机器学习算法的性能。最后，我们讨论了ML方法在ACP预测中的局限性和未来发展方向。