Quantitative structure and bioactivity relationship study on HCV NS5B polymerase inhibitors

Abstract

Several QSAR (quantitative structure–activity relationship) models for predicting the inhibitory activity of 333 hepatitis C virus (HCV) NS5B polymerase inhibitors were developed. All the inhibitors are HCV polymerase non-nucleoside analogue inhibitors (NNIs) fitting into the pocket of the NNI III binding site. For each molecule, global descriptors and 2D property autocorrelation descriptors were calculated from the program ADRIANA.Code. Pearson correlation analysis was used to select the significant descriptors for building models. The whole dataset was split into a training set and a test set randomly or using a Kohonen’s self-organizing map (SOM). Then, the inhibitory activity of 333 HCV NS5B polymerase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) method, respectively. For the test set of the best model (Model 2B), correlation coefficient of 0.91 was achieved. Some molecular descriptors, such as molecular complexity (Complexity), the number of hydrogen bonding donors (HDon) and the solubility of the molecule in water (log S) were found to be very important factors which determined the bioactivity of the HCV NS5B inhibitors. Some other molecular properties such as electrostatic and charge properties also played important roles in the interaction between the ligand and the protein. The selected molecular descriptors were further confirmed by analysing the interaction between two representative inhibitors and the polymerase in their crystal structures.

Keywords:

• HCV NS5B polymerase inhibitors
• Kohonen’s self-organizing map (SOM)
• multilinear regression (MLR)
• support vector machine (SVM)
• quantitative structure–activity relationships (QSAR)

Acknowledgements

This work was supported by the National Natural Science Foundation of China (20975011 and 81273631), International S&T Cooperation Project (S2012ZR0272), and “Chemical Grid Project” of Beijing University of Chemical Technology. We thank Molecular Networks GmbH, Erlangen, Germany for making the programs ADRIANA.Code, Corina, and SONNIA available for our scientific work.