Prediction of potential inhibitors of SARS-CoV-2 using 3D-QSAR, molecular docking modeling and ADMET properties

Abstract

Coronavirus (COVID-19), an enveloped RNA virus, primarily affects human beings. It has been deemed by the World Health Organization (WHO) as a pandemic. For this reason, COVID-19 has become one of the most lethal viruses which the modern world has ever witnessed although some established pharmaceutical companies allege that they have come up with a remedy for COVID-19. To that end, a set of carboxamides sulfonamide derivatives has been under study using 3D-QSAR approach. CoMFA and CoMSIA are one of the most cardinal techniques used in molecular modeling to mold a worthwhile 3D-QSAR model. The expected predictability has been achieved using the CoMFA model (Q 2 = 0.579; R 2 = 0.989; R 2 test= 0.791) and the CoMSIA model (Q 2 = 0.542; R 2 = 0.975; R 2 test= 0.964). In a similar vein, the contour maps extracted from both CoMFA and CoMSIA models provide much useful information to determine the structural requirements impacting the activity; subsequently, these contour maps pave the way for proposing 8 compounds with important predicted activities. The molecular surflex-docking simulation has been adopted to scrutinize the interactions existing between potentially and used antimalarial molecule on a large scale, called Chloroquine (CQ) and the proposed carboxamides sulfonamide analogs with COVID-19 main protease (PDB: 6LU7). The outcomes of the molecular docking point out that the new molecule P1 has high stability in the active site of COVID-19 and an efficient binding affinity (total scoring) in relation with the Chloroquine. Last of all, the newly designed carboxamides sulfonamide molecules have been evaluated for their oral bioavailability and toxicity, the results point out that these scaffolds have cardinal ADMET properties and can be granted as reliable inhibitors against COVID-19.

Assessment of asthma treatment against SARS CoV-2 by using a computer approach

“…The method of using ADMET type filters (Absorption, Distribution, Metabolism, Elimination) and Toxicology, opens up the possibility of filtering and selecting the molecules according to different parameters and their potential toxicity. Additionally, Lipinski's rules allow rapid identification on a large scale of molecules of a "druglike" nature that are more likely to exhibit bioavailability characteristics [17].…”

Quantitative structure–activity relationship-based computational approaches

“…In this examination, the antimalarial action and synthetic designs of 18 carboxamides sulfonamide subsidiaries were taken from the literature. These particles were considered to direct the three-dimensional QSAR examination by parting the information base into two datasets; a preparation set of 14 atoms to foster the quantitative model and a test set of four compounds to affirm the capability of the former model ( Khaldan et al, 2021 ). The following figure demonstrates the SAR established with the help of the developed QSAR model ( Fig.…”

Computational study of quinoline-based thiadiazole compounds as potential antileishmanial inhibitors

“…10,11 Three-dimensional quantitative structure-activity relationships (3D-QSAR) is regarded as one of the most critical procedures in the computational chemistry field. [12][13][14] Its rational purpose is to hunt for places on molecules that could be modified partially or entirely to help boost their activity. 15 Additionally, 3D-QSAR is utilized to set up a linear relationship between descriptors, which are physicochemical features, and biological inhibitory activities.…”

“…They have been applied to broaden the knowledge of the structural properties needed to identify favoured and disfavoured entities for biological activity. 19,20 Predicting ADMET (absorption, distribution, metabolism, excretion, and toxicity) is another critical way to address and determine a compound's safety and bioavailability. Its primary aim is to predict the clinical success of medicine candidates, as inadequate bioavailability is estimated to cause 50% of therapeutic failures.…”