Chinese scientists develop artificial intelligence model to predict pathogenic variants of COVID-19
The new model, named UniBind, has the ability to forecast COVID-19 variants that could increase the virus's infectivity or contribute to the development of resistance to antibodies and vaccines. This is achieved through the analysis of over six million data fragments from viral sequences obtained through global monitoring.
Led by Professor Zhang Kan in the field of medicine, the study emphasizes that UniBind can integrate and analyze information from various experimental sources and conditions. This sets the model apart from most existing AI methods, which can only make predictions based on specific types of experimental data.
The research team reported that using UniBind, they simulated more than 30,000 virtual variants and accurately predicted the evolution of contemporary major strains, such as the XBB and BQ mutations characteristic of the Omicron variant.
An important finding is that the model was able to predict that mutations with high rankings, including A475N and S494K, likely possess a high ability to evade immune responses and could significantly influence the future evolution of the virus.
The study also revealed that the model can accurately predict relationships between different viruses and their mutations. This holds significant value in identifying intermediate hosts of epidemics and forecasting potential pathways for virus transmission between species.
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