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

Study of drug-drug combinations based on molecular descriptors and physicochemical properties.
Comb Chem High Throughput Screen. 2015 Nov 10;
Authors: Niu B, Xing Z, Zhao M, Huo H, Huang G, Chen F, Su Q, Lu Y, Wang M, Yang J, Chen L, Tang L, Zheng L
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.
PMID: 26552439 [PubMed - as supplied by publisher]

Source: Combinatorial Chemistry and High Throughput Screening - November 10, 2015 Category: Chemistry Authors: Niu B, Xing Z, Zhao M, Huo H, Huang G, Chen F, Su Q, Lu Y, Wang M, Yang J, Chen L, Tang L, Zheng L Tags: Comb Chem High Throughput Screen Source Type: research