Computational studies of some benzothiazinone-pepirazine derivatives and lipase b inhibitor for mycobacterium tuberculosis

Computational technique was employed on Benzothiazinone-pepirazine derivatives as dominant anti-mycobacterium tuberculosis. The compound structures were drawn with the aid of chemdraw 3D Pro 12.1.0V and optimized was employed using DFT method applying B3LYP with the 6-31G? basis set. Genetic Function Approximation (GFA) was employed to form five models. Model 1 was sorted out based on model validation parameters and found to be significant with R2 value of 0.948605, R2adj(adjusted correlation coefficient) value of 0.934329, QLoo(Cross validation coefficient) value 0.892724 and R2pred value of 0.658537. The docking studies showed that the ligand 6, 7 and 18 has the highest binding affinities of 10.5, 10.4, 10.3 k/mole are the most vital compounds among the binding scores. Ligand 6 being among the ligands with the highest binding affinity (-10.5 k/mole) was found to be more potent than other compounds. Stability and Robustness the model highlight the way for designing latest Benzothiazinone-pepirazine analogue with better activity against mycobacterium tuberculosis.

QSAR and Lead Optimization

There are many diseases for which suitable drugs have not been identified. As the population increases and the environment gets polluted, new infections are reported. Random screening of synthesized compounds for biological activity is time consuming. QSAR has a prominent role in drug design and optimization. It is derived from the correlation between the physicochemical properties and biological activity. QSAR equations are generated using statistical methods like regression analysis and genetic function approximation. Both 2D parameters and 3D parameters are involved in generating the equation. Among several QSAR equations generated, the best ones are selected based on statistical parameters. Validation techniques usually verify the predictive power of generated QSAR equations. Once the developed QSAR model is validated to be good, the results of that model may be applied to predict the biological activity of newer analogues. This chapter illustrates the various steps in QSAR and describes the significance of statistical parameters and software used in QSAR.

Theoretical study on the reactions of a series of polybromobenzenes with OH radicals: mechanism, kinetics, and QSAR

Polybromobenzenes are a kind of monocyclic aromatic flame retardants that are used as a substitute for polybrominated diphenyl ethers and hexabromocyclododecane. In this paper, the reaction mechanism and rate constants for the reaction of OH radicals with a series of polybromobenzenes such as hexabromobenzene (HBB), 1,2,4,5-tetrabromobenzenes (1,2,4,5-TeBB), pentabromobenzene (PEBB), pentabromoethylbenzene (PBEB), pentabromotoluene (PBT), and 2,4,5-tribromotoluene (2,4,5-TrBT) have been investigated by quantum chemical method. The reaction mechanism was obtained at the MPWB1K/6-311+g(3df,2p)//MPWB1K/6-31+g(d,p) level of theory and the rate constants were deduced over the temperature range of 200–370 K using canonical variational transition state (CVT) theory with the small curvature tunneling (SCT) method. The rate constants of OH radicals with HBB, 1,2,4,5-TeBB, PEBB, PBEB, PBT, and 2,4,5-TrBT are determined to be 5.72 × 10−13, 1.23 × 10−12, 8.78 × 10−13, 9.23 × 10−13, 6.46 × 10−13, and 1.69 × 10−12, respectively, at 298 K and 1 atm. The estimated atmospheric lifetimes of HBB (20.08 days), 1,2,4,5-TeBB (9.65 days), PEBB (13.5 days), PBEB (12.9 days), PBT (18.4 days), and 2,4,5-TrBT (7.0 days) determined by OH radicals indicate that polybromobenzenes have the potential for long-range transport. The genetic function approximation is used to study the quantitative structure–activity relationship. The coefficients indicate that the ELUMO has the highest correlation to logkOH.

QSAR Study on Imidazole Derivatives as Corrosion Inhibitors by Genetic Function Approximation Method

Quantitative structure and activity relationship (QSAR) method is becoming more desirable for predicting of corrosion inhibition properties. The inhibition efficiency of organic compounds is dependent on many basic molecular descriptors, including structural descriptors, thermodynamic descriptors, information content descriptors, topological descriptors as Wiener index, Zagreb index and molecular connectivity indices. A genetic function approximation approach was used to run the regression analysis and establish correlations between different types of descriptors and measured corrosion inhibition efficiency for imidazole derivatives. A QSAR equation was developed and used to predict the corrosion inhibition efficiency for 18 imidazole derivatives. The prediction of corrosion efficiencies of these compounds nicely matched the experimental measurements.