Researchers at Harvard have developed a groundbreaking predictive framework that combines biophysics and artificial intelligence to identify high-risk COVID-19 variants before they pose significant public health threats.

The first study introduced a model that quantitatively links biophysical characteristics of the COVID-19 spike protein to the likelihood of a variant becoming dominant, addressing limitations of previous predictive methods.

This model incorporates epistasis, recognizing that the effects of mutations can depend on one another, which enhances its predictive accuracy.

A second study presented the VIRAL (Viral Identification via Rapid Active Learning) framework, which accelerates the identification of high-risk SARS-CoV-2 variants, focusing lab resources on the most concerning mutations.

VIRAL can identify high-risk variants up to five times faster than traditional methods and requires less than 1% of the experimental effort, greatly improving outbreak response capabilities.

The interdisciplinary approach of these studies combines molecular biophysics, artificial intelligence, and virology, aiming to create a proactive biological forecasting platform applicable beyond infectious diseases, including challenges in cancer biology.

Researchers are also focused on adapting and scaling their framework to tackle challenges posed by other emerging viruses and rapidly evolving tumor cells.

Led by Professor Eugene Shakhnovich, the studies aim to enhance preparedness for pandemics by forecasting viral evolution, enabling timely public health interventions and vaccine adjustments.

The research has received significant support from the National Institutes of Health, underscoring the critical role of federal funding in advancing scientific breakthroughs that address major health issues.

Shakhnovich emphasized that the lab's work aims to better prepare society for new viruses and pandemics by utilizing fundamental principles of physics and chemistry.

The studies, published in the Proceedings of the National Academy of Sciences, aim to enhance proactive measures against emerging viruses by forecasting evolutionary changes in viral variants.

The interdisciplinary research team includes members from Harvard's Department of Chemistry and Chemical Biology, highlighting collaboration across fields such as molecular biophysics, AI, and virology.