期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 19, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/ijms19020467
关键词
drug combination; classification and prediction; improved naive Bayesian algorithm; metabolic enzyme
资金
- National Key Research Program [2016YFA0501703]
- National Natural Science Foundation of China for Young Scholars [31601074, 21403002]
- National Natural Science Foundation of China [31770772, 61772543, U1435222, 61625202, 61272056]
- National Key R&D Program of China [2017YFB0202602, 2017YFC1311003, 2016YFC1302500, 2016YFB0200400, 2017YFB0202104]
- Shanghai Key Laboratory of Intelligent Information Processing [IIPL-2016-005]
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 naive 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 naive Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.
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