Theses (MSc in Analytical and Pharmaceutical Chemistry)

The emergence and re-emergence of viral pathogens such as SARS-CoV-2, human immunodeficiency virus (HIV-1), hepatitis C virus (HCV), Zika virus (ZIKV), and dengue virus (DENV) continue to pose significant global health challenges. Viral proteases play a central role in viral replication through polyprotein processing and therefore represent attractive targets for antiviral drug development. This dissertation investigates the identification of synthetic viral protease inhibitors using an integrated in silico approach comprising Structure-Based Pharmacophore Modelling (SBPM), virtual screening, molecular docking, and pharmacokinetic prediction. High-resolution crystal structures of five disease-specific viral proteases (PDB IDs: 8HUR, 4WF8, 3SPK, 2FOM, and 5H4I) were analyzed to generate validated structure-based pharmacophore models using the Pharmit platform. These models were applied to virtually screen large compound libraries, including ZINC and MolPort, to identify molecules exhibiting key interaction features within protease active sites. The top-ranked virtual hits were further evaluated through molecular docking simulations using iGEMDOCK to predict binding conformations, interaction profiles, and relative binding affinities. Drug-likeness and ADMET properties were assessed using the SwissADME web tool. Several chemically diverse lead scaffolds, including triazine, indazole, and isoquinoline cores, demonstrated favorable hydrogen bonding, π–π stacking, and hydrophobic interactions with conserved catalytic residues across multiple viral proteases. Notably, some compounds exhibited multi-target binding potential, indicating suitability for broad-spectrum antiviral development. Overall, this study demonstrates that SBPM integrated with molecular docking provides an efficient and cost-effective computational framework for antiviral lead identification and establishes a strong foundation for future experimental validation and optimization. Keywords: viral proteases; structure-based drug design; pharmacophore modelling; virtual screening