Computational modeling of tetrahydroimidazo‐[4,5,1‐jk][1,4]‐benzodiazepinone derivatives: An atomistic drug design approach using Kier‐Hall electrotopological state (E‐state) indices

Abstract

Quantitative structure-activity relationships (QSAR), based on E-state indices have been developed for a series of tetrahydroimidazo-[4,5,1-jk]-benzodiazepinone derivatives against HIV-1 reverse transcriptase (HIV-1 RT). Statistical modeling using multiple linear regression technique in predicting the anti-HIV activity yielded a good correlation for the training set (R(2) = 0.913, R(2)(adj) = 0.897, Q(2) = 0.849, MSE = 0.190, F-ratio = 59.97, PRESS = 18.05, SSE = 0.926, and p value = 0.00). Leave-one-out cross-validation also reaffirmed the predictions (R(2) = 0.850, R(2)(adj) = 0.824, Q(2) = 0.849, MSE = 0.328, and PRESS = 18.05). The predictive ability of the training set was also cross-validated by a test set (R(2) = 0.812, R(2)(adj) = 0.799, Q(2) = 0.765, MSE = 0.347, F-ratio = 64.69, PRESS = 7.37, SSE = 0.975, and p value = 0.00), which ascertained a satisfactory quality of fit. The results reflect the substitution pattern and suggest that the presence of a bulky and electropositive group in the five-member ring and electron withdrawing groups in the seven-member ring will have a positive impact on the antiviral activity of the derivatives. Bulky groups in the six-member ring do not show an activity-enhancing impact. Outlier analysis too reconfirms our findings. The E-state descriptors indicate their importance in quantifying the electronic characteristics of a molecule and thus can be used in chemical interpretation of electronic and steric factors affecting the biological activity of compounds.

Computational modeling studies on anti-HIV-1 non-nucleoside reverse transcriptase inhibition by dihydroalkoxybenzyloxopyrimidines analogues: an electrotopological atomistic approach

“…All the 74 compounds were sketched using V-life MDS software [41]. The inhibitory activity pIC 50 Electrotopological State (E-State) indices are widely used in QSAR modeling, including AIDS related research [21,43,44]. The large amount of variables in E-State Indices represent the structural characteristics of molecules such as information about their non-covalent interactions.…”

Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1

“…[30][31] Followed by the pioneering studies regarding quantitativestructure-activity/property relationship (QSAR/QSPR) analysis by Wiener, 32 these mathematical models have established themselves very effectively in quantifying the correlation between selected molecular descriptors and the property of interest. [33][34][35][36] The development of rational QSAR/QSPR models illuminates the necessary features responsible for a better pharmaco-toxicokinetic profile of a drug-like molecule, thereby avoiding irrelevant experimental determinations and can thus serve as an important tool to screen compounds prior to synthesis, thus reducing expensive in vitro and in vivo assays. 37 Although the rigorous model verification methodologies have strengthened the relationship studies, there are still a number of limitations.…”

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

“…[5][6][7] However, the rapid manifestation of these into drug-resistant viral strains has relegated the therapeutic efficacy of the inhibitors. [11][12][13][14][15] The reverse transcriptase (RT) enzyme is essential for the conversion of genetic RNA into DNA, and thus plays a signicant role in the drug discovery pipeline to combat HIV-1 infection. [11][12][13][14][15] The reverse transcriptase (RT) enzyme is essential for the conversion of genetic RNA into DNA, and thus plays a signicant role in the drug discovery pipeline to combat HIV-1 infection.…”

“…[8][9][10] Recent advances in ligand-based and structurebased drug design (LBDD and SBDD) approaches, coupled with virtual screening, are robust tools for the design of newer compounds acting against HIV-1 infection. [11][12][13][14][15] The reverse transcriptase (RT) enzyme is essential for the conversion of genetic RNA into DNA, and thus plays a signicant role in the drug discovery pipeline to combat HIV-1 infection. 16,17 The RT enzyme is a heterodimer made of p66 and p51 subunits, where each subunit contains thumb, palm and nger domains.…”