Study of drug-drug combinations based on molecular descriptors and physicochemical properties

Author(s): Bing Niu, Zhihao Xing, Manman Zhao, Haizhong Huo, Guohua Huang, Fuxue Chen, Qiang Su, Yin Lu, Meng Wang, Jing Yang, Lei Chen, Ling Tang, Linfeng Zheng.

Journal Name: Combinatorial Chemistry & High Throughput Screening

Volume 19 , Issue 2 , 2016

Submit Manuscript
Submit Proposal

Abstract:

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.

Keywords: Physicochemical properties, mRMR, drug-drug combinations, random forest, feature selection.

Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 19
ISSUE: 2
Year: 2016
Page: [153 - 160]
Pages: 8
DOI: 10.2174/1386207319666151110122931
Price: $58

Article Metrics

PDF: 28
HTML: 3
EPUB: 1
PRC: 1