Abstract
In this study, we developed a digital twin for SARS-CoV-2 by integrating diverse data and metadata with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein sequence language modeling. This approach enabled us to computationally design neutralizing antibodies against over 1300 historical strains of SARS-CoV-2, encompassing 64 mutations in the receptor binding domain (RBD) region. 70 AI-designed antibodies were experimentally validated through binding assay and real viral neutralization assays against various strains, including later Omicron strains do not present in the initial design database. 14% of these antibodies exhibited strong reactivity against the RBD of multiple strains, achieving triple cross-binding hit rates using ELISA assay. 10 antibodies neutralized the cytopathic effects (CPE) of the Delta strain at IC50 values of < 10 µg/ml, and one antibody neutralized the CPE of Omicron. These findings demonstrate the potential of our approach to influence future therapeutic design for existing virus strains and predict hidden patterns in viral evolution that AI can leverage to develop emerging antiviral treatments.