Cefprozil

The dengue virus (DENV) infects approximately 400 million people annually worldwide causing significant morbidity and mortality. Despite advances in understanding the virus life cycle and infectivity, no specific treatment for this disease exists due to the lack of therapeutic drugs. In addition, vaccines available currently are ineffective with severe side effects. Therefore, there is an urgent need for developing therapeutics suitable for effective management of DENV infection. In this study, we adopted a drug repurposing strategy to identify new therapeutic use of existing FDA approved drug molecules to target DENV2 non-structural proteins NS3 and NS5 using computational approaches. We used Drugbank database molecules for virtual screening and multiple docking analysis against a total of four domains, the NS3 protease and helicase domains and NS5 MTase and RdRp domains. Subsequently, MD simulations and MM-PBSA analysis were performed to validate the intrinsic atomic interactions and the binding affinities. Furthermore, the internal dynamics in all four protein domains, in presence of drug molecule binding were assessed using essential dynamics and free energy landscape analyses, which were further coupled with conformational dynamics-based clustering studies and cross-correlation analysis to map the regions that exhibit these structural variations. Our comprehensive analysis identified tolcapone, cefprozil, delavirdine and indinavir as potential inhibitors of NS5 MTase, NS5 RdRp, NS3 protease and NS3 helicase functions, respectively. These high-confidence candidate molecules will be useful for developing effective anti-DENV therapy to combat dengue infection.

The entropy of the chem. graph primarily measures the complexity of chem. structures.In this study, Shannon′s entropy approach was utilized to calculate entropies for chem. graphs with degree-based topol. indexes, predicting the physicochem. correlation capability of 12 drugs for kidney infection and its implications for physicochem. aspects.Entropy can help identify important correlations between the structure and function of compounds, which in turn can aid in developing QSPR models.A computer-based computing technique and algorithm were employed to simplify calculations and data anal.Degree-based topol. indexes, along with the entropy graphs weighted with indexes, were calculated using the MATLAB program and used to measure entropy.This research aims to predict the physicochem. characteristics of these drugs based on the entropy of topol. indexes.Several regression models were used to analyze drugs′ quant. structure-property relationship (QSPR) and predict their physicochem. properties.Effective predictors were identified in this study, and optimal equations were introduced to evaluate the physicochem. characteristics based on the highest correlation coefficient and Fisher′s statistical test.Interestingly, in some cases, the entropy of degree-based indexes showed a stronger correlation with drug characteristics.In conclusion, the optimal equations for estimating molar refractivity (MR) and polarizability (P) using the ENTRR-1/2,ENTSC, and ENTABC indexes incorporate compound, growth, and exponential factors.Meanwhile, the optimal equations for estimating molar volume (MV) with the ENTF index are quadratic and cubic.