Harnessing AI and Biophysics to Predict High-Risk Viral Variants Ahead of Outbreaks

In recent advancements in infectious disease research, scientists have integrated the disciplines of biophysics and artificial intelligence to enhance our ability to forecast the emergence of highly concerning viral variants before they cause widespread outbreaks. This novel approach addresses a critical challenge: when a new COVID-19 variant is detected, determining whether it will be more contagious or severe often takes time, leaving public health responses delayed. The delay hampers efforts to update vaccines and implement containment strategies effectively.

A groundbreaking study published in the Proceedings of the National Academy of Sciences showcases how a team from Harvard's Department of Chemistry and Chemical Biology has developed a predictive model that combines fundamental physical principles with machine learning techniques. The researchers aimed to anticipate viral evolution, particularly focusing on the spike protein of COVID-19, analyzing how specific mutations influence viral fitness and immune evasion. This multiscale model incorporates complex interactions, including epistasis, where the effect of one mutation depends on others, significantly improving prediction accuracy.

The study's lead scientist, Professor Eugene Shakhnovich, emphasized that understanding viral evolution through physics-based modeling provides a proactive tool for identifying potential threats. Alongside the primary model, the team also introduced VIRAL (Viral Identification via Rapid Active Learning), a computational framework that accelerates the detection of high-risk SARS-CoV-2 variants.

VIRAL employs artificial intelligence to analyze possible spike mutations, pinpointing those most likely to increase transmissibility or antibody resistance. This process allows laboratories to concentrate their efforts on testing the most dangerous variants, reducing time and resource expenditure. Results indicate that this system can identify concerning variants up to five times faster than traditional testing approaches, with less than 1% of the usual experimental screening required.

This integrated approach signifies a shift from reactive to predictive viral surveillance, enabling health authorities to implement preemptive measures. The models and frameworks are designed to extend beyond COVID-19, with potential applications in predicting the evolution of other emerging viruses and rapidly adapting tumor cells in cancer biology.

The research highlights the importance of interdisciplinary collaboration, uniting physics, biology, and machine learning to better understand and combat rapidly evolving biological threats. Professor Shakhnovich stresses that such predictive tools could revolutionize how we respond to infectious diseases, making global health responses more efficient and effective.

Funding from the National Institutes of Health has supported this innovative work, underlining the significance of sustained investment in fundamental science for public health preparedness. As viruses continue to evolve, these advanced forecasting models offer a promising avenue for staying one step ahead and protecting populations worldwide.

For more details, see the original studies:

• Dianzhuo Wang et al., "Biophysical principles predict fitness of SARS-CoV-2 variants," Proceedings of the National Academy of Sciences, 2024.
• Marian Huot et al., "Predicting high-fitness viral protein variants with Bayesian active learning and biophysics," Proceedings of the National Academy of Sciences, 2025.