AI-based antibody discovery platform identifies novel, diverse, and pharmacologically active therapeutic antibodies against multiple SARS-CoV-2 strains

Background: We are entering a new era of antibody discovery and optimization where machine learning (ML) processes will become indispensable for the design and development of therapeutics.

Methods: We have constructed a Humanoid Antibody Library for the discovery of therapeutics that is an initial step towards leveraging the utility of artificial intelligence and ML. We describe how we began our validation of the library for antibody discovery by isolating antibodies against a target of pandemic concern, SARS-CoV-2. The two main antibody quality aspects that we focused on were functional and biophysical characterization.

Results: The applicability of our platform for effective therapeutic antibody discovery is demonstrated here with the identification of a panel of human monoclonal antibodies that are novel, diverse, and pharmacologically active.

Conclusions: These first-generation antibodies, without the need for affinity maturation, exhibited neutralization of SARS-CoV-2 viral infectivity across multiple strains and indicated high developability potential.