Computational insight to design new potential hepatitis C virus NS5B polymerase inhibitors with drug-likeness and pharmacokinetic ADMET parameters predictions

Background: Hepatitis C virus (HCV) is considered a worldwide health problem since it affects over 3% of the population and causes 300,000 fatalities per year. Chronic infection causes high morbidity and mortality in patients, leading to liver cirrhosis, hepatocellular carcinoma, fibrosis, liver cancer, and other HCV-related illnesses. Finding novel and better HCV treatments is a top international health goal right now. As a result, the pressing need for new HCV antiviral drugs has fueled research into the structural requirements of NS5B polymerase inhibitors at a molecular basis. Results: In this study, an in silico technique was applied to study 79 compounds with HCV inhibitory bioactivity, with the best statistical results (R-2 = 0.7051, Q(2) = 0.6455, R-pred(2) = 0.6992, R-c(r)2 = 0.6570, SEE = 0.2694). Conclusions: This QSAR investigation allowed the research team to evaluate the influence of straightforward descriptors, shedding insight into the critical elements that guide the design of innovative effective molecules. Most of the innovative effective molecules exhibited better binding affinity (-195.6 kcal/mol) than dasabuvir the reference drug (-171.0 kcal/mol) with the target receptor (hepatitis C virus NS5B RNA polymerase). ADMET prediction disclosed enhanced pharmacokinetic properties with lower MRTD (maximum tolerated dose) of some new derivatives.