MLR-Based QSAR Models for Predicting Inhibitory Activity of Reverse Transcriptase by HEPT Derivatives using GETAWAY Descriptors

In this study, quantitative structure-activity relationship (QSAR) models for non-nucleoside reverse transcriptase inhibitors based on 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine (HEPT) derivatives were generated. The structures of the compounds and their activities were obtained from the literature. The data set were divided into two sets: training set (N=91) and validating set (N=10). All 3-D structures of these inhibitors were optimized by semi-empirical method, AM1 prior to calculations of 3-D molecular descriptors, GETAWAY. Multiple linear regression (MLR) using stepwise method was applied to determined significant descriptors. Out of 197 GETAWAY descriptors, 4-14 molecular descriptors have significant relationships with the activities (expressed as log (1/EC50)) of HEPT. The MLR method generated 14 models. The predictive power of these models were evaluated internally by applying the following statistical parameters for the training set and test set: root-mean-square error for prediction (RMSE), correlation coefficient (R), squared correlation coefficient (R2), adjusted squared correlation coefficient (R2adj), difference between R2 and R2adj (R2 – R2adj), squared cross-validation correlation coefficient (Q2). External validation was performed by employing Golbraikh and Tropsha criteria. Moreover, residual analysis was performed. Internal validation of Model XX (N = 91) revealed that it has the highest predictive power (RMSE = 0.4288, R = 0.9393, R2 = 0.882, R2adj = 0.8620, R2 - R2adj = 0.0203, Q2 = 0.8317). However, external validation (using the validating set, N=10) showed that Model XII has the highest predictive power (R2 = 0.961, R20 = 0.9565, k = 0.8648, k’ = 0.9800, [R2 - R20] = 0.0066, [R2 - R20] /R2 = 0.0069, R2pred = 0.9481) based on Golbraikh and Tropsha criteria. Residual analysis confirmed that both models are valid.

QSAR study for anti-HIV-1 activities of HEPT derivatives using MLR and PLS

A QSAR study using Multiple Linear Regression (MLR) and a Partial Least Squares (PLS) methodology was performed for a series of 127 derivatives of 1-(2-hydroxy-ethoxy)methyl]-6-(phenylthio)-timine (HEPT), a potent inhibitor of the of the human immunodeficiency virus type 1, HIV-1 reverse transcriptase (RT). To explore the relationship between a pool of HEPT derivative descriptors (as independent variables) and anti-HIV-1 activity expressed as log (1/EC50), as dependent variable) MLR and PLS methods have been employed. Using Dragon descriptors, the present study aims to develop a predictive and robust QSAR model for predicting anti-HIV activity of the HEPT derivatives for better understanding the molecular features of these compounds important for their biological activity. According to the squared correlation coefficients, which had values between 0.826 and 0.809 for the MLR and PLS methods, the results demonstrate almost identical qualities and good predictive ability for both MLR and PLS models. After dividing the dataset into training and test sets, the model predictability was tested by several parameters, including the Golbraikh-Tropsha external criteria and the goodness of fit tested with the Y-randomization test.