Molecular design of antioxidant lubricating oil additives via QSPR and analysis dynamic simulation method.

Alternatives antioxidant lubricant additives have been proposed by many researchers to replace long-time use of multifunctional lubricant additive, Zinc-dialkyl-dithiophosphate (ZDDP). Computational methods (QSPR and MD) were successfully used to design five novel anti-oxidant lubricating oil additives with improved properties and dynamic binding energies. The five novel antioxidant lubricant additives with improved properties and without sulfated ash, phosphorus, and sulfur (SAPS) were successfully designed. These group of newly designed additives were better than other similar research from the literature and could stop or terminate complete oxidation of the lubricant. Moreover, the result of molecular dynamics simulations (MD) in which 3-(2-(3-amino-4,5-dihydroxyphenyl)-3-chloro-4-oxoazetidin-1-yl)-2-argioquinazolin-4(3H)-one with the most promised dynamic binding energy of -1487.68 kcal/mol was found to be dynamically bound better on the simulated steel coated surface than the DLC coated surface and was also revealed to be excellently good when compared with commercially sold multifunctional additives, ZDDP (197.143 kcal/mol). These groups of five newly designed additives could be easily synthesized in the wet laboratory by adding -OH and or NH2 around the ortho, meta and para position of the phenyl group of the structure template. This research will help designing new oxidation resistance lubricating oil additives with improved properties that will reduce the capacity of base oil to oxidize and form sludge during the autoxidation process of the lubricating oil.

Quantitative structure-activity relationship and molecular docking studies of a series of quinazolinonyl analogues as inhibitors of gamma amino butyric acid aminotransferase.

Quantitative structure-activity relationship and molecular docking studies were carried out on a series of quinazolinonyl analogues as anticonvulsant inhibitors. Density Functional Theory (DFT) quantum chemical calculation method was used to find the optimized geometry of the anticonvulsants inhibitors. Four types of molecular descriptors were used to derive a quantitative relation between anticonvulsant activity and structural properties. The relevant molecular descriptors were selected by Genetic Function Algorithm (GFA). The best model was validated and found to be statistically significant with squared correlation coefficient (R2) of 0.934, adjusted squared correlation coefficient (R2adj) value of 0.912, Leave one out (LOO) cross validation coefficient (Q2) value of 0.8695 and the external validation (R2pred) of 0.72. Docking analysis revealed that the best compound with the docking scores of -9.5 kcal/mol formed hydrophobic interaction and H-bonding with amino acid residues of gamma aminobutyric acid aminotransferase (GABAAT). This research has shown that the binding affinity generated was found to be better than the commercially sold anti-epilepsy drug, vigabatrin. Also, it was found to be better than the one reported by other researcher. Our QSAR model and molecular docking results corroborate with each other and propose the directions for the design of new inhibitors with better activity against GABAAT. The present study will help in rational drug design and synthesis of new selective GABAAT inhibitors with predetermined affinity and activity and provides valuable information for the understanding of interactions between GABAAT and the anticonvulsants inhibitors.