Computational and pharmacokinetics studies of 1,3-dimethylbenzimidazolinone analogues of new proposed agent against Alzheimer's disease

Abstract

Abstract Background Alzheimer's disease (AD) is a multifactorial disorder that gradually destroys wisdom and memory skills. Currently, this disease can only be treated palliatively. However, the molecular mechanisms underlying this condition remain elusive. Therefore, these treatments are inadequate. Current medications can only increase patient warning signs. Chemical structures were drawn using Chemsketch software. Spartan’14 software was used to optimize the structures using density functional theory (DFT). The PaDEL software was used to generate the descriptors. The genetic function algorithm (GFA) and multi-linear regression (MLR) approaches were used to generate the QSAR model. Results In the present study, we performed a computational investigation, molecular docking, and pharmacokinetics analysis of 1,3-dimethylbenzimidazolinone derivatives. The descriptors generated in the model are AATS7i, MATS5p, SpMin7_Bhe, and GATS6c. Compounds 13 and 21 have the best binding scores, 11.2 kcal/mol and 10.8 kcal/mol, respectively, and optimal protein–ligand interactions with AChE. These compounds have brilliant pharmacokinetic and physicochemical properties. Conclusions The model was validated and found to have good internal and external assessment parameters: R2 = 0.937, $$R_{{{\text{adj}}}}^{2}$$ R adj 2  = 0.863, $$Q_{{{\text{cv}}}}^{2}$$ Q cv 2  = 0.788, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.756, LOF = 0.0268, $$cR_{{\text{p}}}^{2}$$ c R p 2  = 0.677. In summary, these data suggested that compounds 13 and 21 are promising multifunctional agents against AD.