• Yan Hu, Yi Lu, WangShuoSSchool of Life Sciences, Shanghai University, Shanghai 200444, China., Mengying Zhang, Xiaosheng Qu, and Bing Niu.
• School of Life Sciences, Shanghai University, Shanghai 200444, China.
• Curr Drug Targets. 2019 Jan 1; 20 (5): 488-500.

BackgroundGlobally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics.ObjectiveIn this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed.ResultsMachine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design.ConclusionThis paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

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