Computer-aided approaches for human norovirus drug discovery: a comprehensive review

Human norovirus is the leading global cause of viral acute gastroenteritis, with an estimated ~685 million cases and ~200,000 deaths annually. No licensed antivirals or vaccines are currently available. Despite historical limitations in robust in vitro models, structure and ligand-based computational approaches - supported by protease and polymerase crystal structures - have identified multiple chemotypes as potential antivirals.

This review provides an overview of all studies reported to date, indexed in public databases, in which computer-aided drug discovery (CADD) techniques have been employed. The authors report the computational methodologies used, the chemical structures of the identified compounds, and, if available, their biological activities. Where in silico results lack experimental validation, the authors highlight limitations and propose minimal validation assays.

The absence of 3D structures for most viral proteins has limited the identification of novel chemotypes through CADD approaches. Furthermore, the lack of biological validation after in silico studies may slow down progress in this field, as researchers might focus on compounds that seem promising only at the computational level. Emerging systems such as human intestinal enteroids, together with AI/ML augmented CADD, can accelerate optimization and triage of non-nucleoside and covalent protease inhibitors.