A Review of In Silico Approaches for Discovering Natural Viral Protein Inhibitors in Aquaculture Disease Control.

Abstract

Viral diseases pose a significant threat to the sustainability of global aquaculture, causing economic losses and compromising food security. Traditional control methods often demonstrate limited effectiveness, highlighting the need for alternative approaches. The integration of computational methods for the discovery of natural compounds shows promise in developing antiviral treatments. This review critically explores how both traditional and advanced in silico computational techniques can efficiently identify natural compounds with potential inhibitory effects on key pathogenic proteins in major aquaculture pathogens. It highlights fundamental approaches, including structure-based and ligand-based drug design, high-throughput virtual screening, molecular docking, and absorption, distribution, metabolism, excretion and toxicity (ADMET) profiling. Molecular dynamics simulations can serve as a comprehensive framework for understanding the molecular interactions and stability of candidate drugs in an in silico approach, reducing the need for extensive wet-lab experiments and providing valuable insights for targeted therapeutic development. The review covers the entire process, from the initial computational screening of promising candidates to their subsequent experimental validation. It also proposes integrating computational tools with traditional screening methods to enhance the efficiency of antiviral drug discovery in aquaculture. Finally, we explore future perspectives, particularly the potential of artificial intelligence and multi-omics approaches. These innovative technologies can significantly accelerate the identification and optimisation of natural antivirals, contributing to sustainable disease management in aquaculture.