Anticancer peptides (ACPs) eliminate pathogenic bacteria and kill tumor cells, showing no hemolysis and no damages to normal human cells. This unique ability explores the possibility of ACPs as therapeutic delivery and its potential applications in clinical therapy. Identifying ACPs is one of the most fundamental and central problems in new antitumor drug research. During the past decades, a number of machine learning-based prediction tools have been developed to solve this important task. However, the predictions produced by various tools are difficult to quantify and compare. Therefore, in this article, a comprehensive review of existing machine learning methods for ACPs prediction and fair comparison of the predictors is provided. To evaluate current prediction tools, a comparative study was conducted and analyzed the existing ACPs predictor from the 10 public works of literature. The comparative results obtained suggest that the Support Vector Machine-based model with features combination provided significant improvement in the overall performance when compared to the other machine learning method-based prediction models.

抗癌肽（ACP）可消除病原菌并杀死肿瘤细胞，不会出现溶血现象，对正常人体细胞也不会造成损害。这种独特的能力探索了 ACP 作为治疗递送的可能性及其在临床治疗中的潜在应用。识别ACP是抗肿瘤新药研究中最基本、最核心的问题之一。在过去的几十年中，已经开发了许多基于机器学习的预测工具来解决这一重要任务。然而，各种工具产生的预测很难量化和比较。因此，在本文中，对现有的 ACP 预测机器学习方法进行了全面回顾，并对预测变量进行了公平比较。为了评估当前的预测工具，我们对 10 篇公共文献中的现有 ACP 预测器进行了比较研究和分析。获得的比较结果表明，与其他基于机器学习方法的预测模型相比，基于支持向量机的特征组合模型在整体性能方面提供了显着的改进。