The index of ideality of correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity

Abstract

Computational prediction of HIV-1 protease inhibitor via quantitative structure–activity relationships (QSARs) is a popular study in the field of computational chemistry. The aim of the present study was building up of QSAR models for anti-HIV activity by means of the CORAL software ( http://www.insilico.eu/coral ). Applying of correlation weights for five and six-member molecular rings as components of the target function in the Monte Carlo optimization that is aimed to build up correlation between activity of HIV-1 protease inhibitors expressed as pIC50 = lg[1/(IC50 × 109)] and optimal descriptor improves the predictive potential. Simplified molecular input-line entry system (SMILES) is used as the representation of the molecular structure of HIV-1 protease inhibitors for the QSAR analysis. New criterion of predictive potential so-called index of ideality of correlation (IIC) has been appllied to improve the model for HIV-1 protease inhibitors activity. High correlation coefficients were observed between the experimental and predicted anti-HIV activity. Applying of special ring code as component of the Monte Carlo calculation significantly improves the statistical quality of the model. Furthermore, applying of the IIC as component of the Monte Carlo optimization improves the predictive potential of the CORAL models. The presence of the rings and the quality of these are very important molecular features which are able to improve statistical quality of model for anti-HIV activity. In addition, the ability of IIC to improve predictive potential of a model has been confirmed.