Study of drug-drug combinations based on molecular descriptors and physicochemical properties
Bing Niu, Zhihao Xing, Manman Zhao, Haizhong Huo, Guohua Huang, Fuxue Chen, Qiang Su, Lu Yin, Meng Wang, Jing Yang, Lei Chen, Ling Yin Tang, Linfeng Zheng · Combinatorial Chemistry & High Throughput Screening · 2015
In the present study, molecular descriptors and physicochemical properties were used to encode drug molecules. Based on this molecular representation method, Random forest was applied to construct a drug-drug combination network. After feature selection, an optimal features subset was built, which described the main factors of drugs in our prediction. As a result, the selected features can be clustered into three categories: elemental analysis, chemistry, and geometric features. And all of the three types features are essential elements of the drug-drug combination network. The final prediction model achieved a Matthew's correlation coefficient (MCC) of 0.5335 and an overall prediction accuracy of 88.79% for the 10-fold cross-validation test.