Quantitative structure–activity relationship (QSAR) and molecular docking of xanthone derivatives as anti-tuberculosis agents

Abstract

Quantitative structure–activity relationship (QSAR) and molecular docking approach were carried out to design novel anti-tuberculosis agents based on xanthone derivatives. QSAR designed new compounds were calculated by Austin Model 1 (AM1) methods and analysis of multi-linear regression (MLR). The result showed that the best model as follows: Log IC50 = 3.113 + 11.627 qC1 + 15.955 qC4 + 11.702 qC9, this result has appropriate some statistical parameters (PRESS = 2.11, r2 = 0.730, SEE = 0. 3545, R = 0.6827, FCal/FTab = 4.68), and being used to design a potential anti-tuberculosis drugs with substituted amide, sulfoxide, and carboxylate group xanthone scaffold by a number of their inhibitory concentration (IC50). The mechanism action of sulfonamide substituted on the xanthone scaffold as anti-tuberculosis was carried out using molecular docking. Docking inhibition studies were carried out on MTB C171Q receptor (4C6X.pdb) as KasA inhibitors using by the discovery studio. Based on the binding interaction showed, the sulfonamide substituted xanthone has potential being the anti-tuberculosis drugs by KasA inhibitor for target drug activity.

QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones against Mycobacterium tuberculosis

“…It is important to note that the mechanism of action of the selected structures was not taken into account for the construction of the data, since it is not a requirement for the elaboration of QSAR models according to the different research related to this methodology [13,[89][90][91][92][93][94][95][96]. Nevertheless, the structural diversity of the compounds studied here, with molecular structures similar to compounds already reported as antituberculosis (fluoroquinolones and quinolones), in addition to the great diversity of isosteric substituents on the pharmacophoric nuclei, guarantee a domain of application suitable for the rational design of new drugs.…”

“…Molecular docking is a versatile tool that allows finding minimum energies of proteinligand interaction structures [116] and has been used as a standard tool for designing and synthesizing new potential anti-TB compounds [69,89,[117][118][119][120][121].…”

Modeling the Relationship of Net Atomic Charge with the Activity of 5-Aminopyrazole Derivative Compounds as Antioxidants with AM1 Method

“…Multilinear regression was performed using the SPSS program which selected the independent variable data with Log IC50. The data from the multilinear regression analysis resulted in the QSAR model equation consisting of the 6 best descriptors where the results from the QSAR equation model obtained the values of R, R2, SE (Standard Error), Sig and Fhit, and PRESS [22]. The selection of the best QSAR equation model is carried out by taking into account the steric parameters, namely the price of R, R2, adjusted R2, SE, Sig, Fhit/Ftab, and PRESS.…”

“…If the R value is closer to 1, the relationship between the independent variable and the dependent variable is getting stronger, meaning that the 6 descriptors involved in the regression model together have a strong relationship to the activity of the 5-aminopyrazole derivative. The R Square value of 0.524 describes the effect of the independent variable (descriptor) on the dependent variable (biological activity) [22]. The SE (Standard Error) value is a parameter measuring the value of the error variation in the experiment [20].…”

Quantitative Structure-Activity Relationship of 3-Thiocyanate-1H-Indoles Derived Compounds as Antileukemia by AM1, PM3, and RM1 Methods

“…The selection of the best QSAR equation requires that the correlation coefficient (r) be greater than 0.8, the Fcount value should exceed the Ftable (Fcount/Ftable>1) for a 95% confidence level, and the model should have a small standard error (SE) and a small predict the residual sum of square (PRESS) value [11]. These QSAR equation models underwent statistical testing based on the values of r, r 2 , SE, and F [32], and the PRESS value was calculated for the four test compounds [33]. Model 6 was identified as the best for the AM1, PM3, and RM1 methods because it had an r-value in the range of 0.9, the smallest PRESS value, and the highest Fcount/Ftable value [34].…”