Researchers at Washington State University have used artificial intelligence and molecular simulations to identify a single molecular interaction that viruses rely on to enter human cells.
The study targets viral infection at its earliest stage, differing from most antiviral treatments that act only after a virus has already entered a cell.
Published in November in the journal Nanoscale, the research focused on viral entry, one of the most complex and least understood phases of infection.
Scientists applied AI to analyse thousands of molecular interactions and narrow them down to one critical target essential for viral entry.
Laboratory experiments showed that disrupting this interaction prevented viruses from entering new cells.
Herpes viruses were used as a test case because their entry mechanisms are well documented and relevant to other viral families.
These viruses depend on a surface fusion protein called glycoprotein B, which enables fusion with host cell membranes.
AI simulations helped researchers identify which internal interaction within this large protein was most crucial for infection.
“Viruses attack cells through thousands of interactions, and our research is to identify the most important one so we can stop the virus from getting into the cell,” Professor Jin Liu said.
Liu said traditional trial-and-error methods are slow and costly compared with AI-driven simulations.
The project began after the COVID-19 pandemic and was led by Professor Anthony Nicola with funding from the National Institutes of Health.
Researchers say the approach remains early-stage but could guide future antiviral drug development.
The same computational framework could also be applied to diseases driven by faulty protein interactions, including Alzheimer’s disease.