Computational models on quantitative prediction of bioactivity of HIV-1 integrase 3’ processing inhibitors

Abstract

In this study, four computational quantitative structure–activity relationship (QSAR) models were built to predict the bioactivity of 3’ processing (3’P) inhibitors of HIV-1 integrase. Some 453 inhibitors whose bioactivity values were detected by the radiolabelling method were collected. The molecular structures were represented with MOE descriptors. In total, 21 descriptors were selected for modelling. All inhibitors were divided into a training set and a test set with two methods: (1) by a Kohonen’s self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the training/test set divided by SOM, the correlation coefficients (r) were over 0.84, and for the training/test set split randomly, the r values were over 0.86. Some molecular properties such as hydrogen bond donor capacity, atomic partial charge properties, molecular refractivity, the number of aromatic bonds and molecular surface area, volume and shape properties played important roles for inhibiting 3’ processing step of HIV-1 integrase.

Keywords:

• HIV-1 integrase 3’P inhibitors (HIV-1 IN3’PIs)
• quantitative structure–activity relationship (QSAR)
• support vector machine (SVM)
• mutilinear regression (MLR)
• self-organizing map (SOM)

Acknowledgements

This work was supported by the National Natural Science Foundation of China (20975011 and 21375007) and “Chemical Grid Project” of Beijing University of Chemical Technology. We thank Molecular Networks GmbH, Erlangen, Germany for making the programs CORINA and SONNIA available for our scientific work.