摘要
背景：全球癌症患者和死亡人数每年都在不断增加，因此癌症已经成为世界上发病率和死亡率最高的原因之一。近年来，抗肿瘤药物的研究已成为最热门的医学课题之一。 目的：为了研究机器学习在抗癌药物活性预测中的应用，选择线性判别分析（LDA）、主成分分析（PCA）、支持向量机（SVM）、随机森林（RF）、K最近邻（KNN）、朴素贝叶斯（NB）等机器学习方法。并列举了其在抗癌药物设计中的应用实例。 结果：机器学习有助于抗癌药物的设计，节省研究人员的时间和成本。但是，它只能作为药物设计的辅助工具。 结论：介绍了机器学习方法在抗癌药物设计中的应用。讨论了在抗癌药物活性预测领域中成功识别和预测的许多实例，抗癌药物的研究仍在积极进行中。此外，还介绍了一些与抗癌药物相关的Web服务器的优点。
关键词: Machine Learning（ML），Antantancer 33647；；S，Linaar Discriminant Analysis（LDA），Principal Components Analysis（PCA），Support Vector Machine（SVM），Random Forest（RF），K-Nearest Neighbor（KNN），Naive Bayes（NB），Deep Learning，Web Serders.
图形摘要
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