Conformational analysis and QSAR modeling of 14-membered macrolide analogues against mycobacterium tuberculosis

Abstract

Electronic structures, effect of the substitution and structure physical-chemistry properties relationship for macrolide derivatives, have been studied by PM3 and ab initio methods. In the present work, the calculated values, namely net charges, bond lengths, MESP, dipole moments, electron-affinities, heats of formation, then, we treated the structural, physical and chemical relationships for a series of macrolide derivatives with inhibition activity against Mycobacterium tuberculosis. QSAR studies were done for these macrolide derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of macrolide derivatives. Results validate the derived QSAR model.

Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations

“…The coefficient correlation’s predictive error PE is a measure used to assess the predictive capacity of the resulting model. The generated model met the requirement of R > 6PE, indicating that it has strong predictive power [ 21 ] ( Table 1 ).…”

“…The coefficient correlation's predictive error PE is a measure used to assess the predictive capacity of the resulting model. The generated model met the requirement of R > 6PE, indicating that it has strong predictive power [21] (Table 1). The results of the Y-randomization after 50 different randomization trials show the R 2 and Q 2 to have low values compared to those of the original model as indicated in Table 3, which shows the robustness of the model.…”

“…Finally, we used the multiple linear regression (MLR) method to predict the biological activity of the data set as a linear function of the structural properties, which correlates the ordinate y (pIC 50 ) as a dependent variable to the molecular descriptors xi [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. The analysis was conducted using SPSS version 19 for Windows [ 42 ].…”