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

  • Alex A. Tardaguila Department of Physical Science, Pamantasan ng Lungsod ng Maynila, Intramuros, Manila
  • Jennifer C. Sy Department of Physical Science, Pamantasan ng Lungsod ng Maynila, Intramuros, Manila
  • Marielyn R. Omañada Department of Physical Science, Pamantasan ng Lungsod ng Maynila, Intramuros, Manila
  • Eric R. Punzalan Department of Chemistry, De La Salle University, Malate, Manila
Keywords: QSAR, HIV, reverse transcriptase, HEPT, MLR

Abstract

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.

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Published
2013-07-01
How to Cite
Tardaguila, A. A., Sy, J. C., Omañada, M. R., & Punzalan, E. R. (2013). MLR-Based QSAR Models for Predicting Inhibitory Activity of Reverse Transcriptase by HEPT Derivatives using GETAWAY Descriptors. KIMIKA, 24(2), 2-17. https://doi.org/10.26534/kimika.v24i2.2-17
Section
Research Articles