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）消除了病原细菌并杀死了肿瘤细胞，没有溶血现象，也没有破坏正常的人类细胞。这种独特的能力探索了ACP作为治疗性药物的可能性及其在临床治疗中的潜在应用。在新的抗肿瘤药物研究中，识别ACP是最基本和核心的问题之一。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。但是，由各种工具产生的预测难以量化和比较。因此，在本文中，将对现有的用于ACP预测的机器学习方法进行全面回顾，并对预测器进行公平比较。要评估当前的预测工具，进行了比较研究，并从10篇公共文献中分析了现有ACP的预测因子。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，具有特征组合的基于支持向量机的模型在整体性能上有显着改善。