State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
College of Computer Science and Electronic Engineering & National Supercomputing Centre in Changsha, Hunan University, Changsha 410082, China.
Int J Mol Sci. 2018 Feb 5;19(2):467. doi: 10.3390/ijms19020467.
Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.
药物组合疗法由于其副作用较少、毒性较低、疗效较好，是治疗复杂疾病的一种很有前途的策略。然而，由于市场上批准的药物数量不断增加，在可能的组合的广阔空间中确定所有有效的药物组合是不可行的，因为识别有效药物组合的实验方法既费时又费力。在这项研究中，我们对各种类型的特征进行了系统分析，以描述药物对。这些特征包括药物的靶点信息、药物靶点蛋白所涉及的通路、药物的副作用、药物的代谢酶和药物转运体。后两个特征（代谢酶和药物转运体）与药物的代谢和转运特性有关，这在以前的研究中没有进行分析或使用。然后，我们设计了一种新颖的改进朴素贝叶斯算法，利用上述提到的单个类型的特征来构建分类模型，以预测有效的药物组合。我们的结果表明，我们提出的方法的性能确实优于朴素贝叶斯算法和其他传统分类算法，如支持向量机和 K-最近邻。
