HCV NS3 serine protease as a drug target for the development of drugs against hepatocellular carcinoma (liver cancer)

Human viral oncogenesis is a complex phenomenon and a major contributor to the global cancer burden. Several recent findings revealed cellular and molecular pathways that promote the development and initiation of malignancy when viruses cause an infection. Even, antiviral treatment has become an approach to eliminate the viral infections and prevent the activation of oncogenesis. Therefore, for a better understanding, the molecular pathogenesis of various oncogenic viruses like, hepatitis virus, human immunodeficiency viral (HIV), human papillomavirus (HPV), herpes simplex virus (HSV), and Epstein-Barr virus (EBV), could be explored, especially, to expand many potent antivirals that may escalate the apoptosis of infected malignant cells while sparing normal and healthy ones. Moreover, contemporary therapies, such as engineered antibodies antiviral agents targeting signaling pathways and cell biomarkers, could inhibit viral oncogenesis. This review elaborates the recent advancements in both natural and synthetic antivirals to control viral oncogenesis. The study also highlights the challenges and future perspectives of using antivirals in viral oncogenesis.

show abstract

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES

Meewan,

Panmanee,

Petchyam

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules’ simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

HCV NS3 serine protease as a drug target for the development of drugs against hepatocellular carcinoma (liver cancer)

Cited by 3 publications

References 55 publications

Recent studies of nitrogen containing heterocyclic compounds as novel antiviral agents: A review

Recent studies of nitrogen containing heterocyclic compounds as novel antiviral agents: A review

Recent Updates on Viral Oncogenesis: Available Preventive and Therapeutic Entities

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES

Contact Info

Product

Resources

About