Machine learning platform generates novel COVID-19 antibody sequences for experimental testing

Lawrence Livermore National Laboratory (LLNL) researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2, the virus that causes COVID-19. The research team is performing experimental testing on the chosen antibody designs. Currently, treating COVID-19 with antibodies is only possible by harvesting them from the blood of patients who have fully recovered. As the new antibody designs are improved through an iterative computational-experimental process, they could enable a safer, more reliable and scalable pathway to using antibodies as potential treatments for people stricken with the disease, scientists said. In a paper appearing on the open access preprint website BioRxiv--which has not been peer-reviewed--LLNL scientists describe how they used the Lab's high performance computers and a machine learning-driven computational platform to design antibody candidates predicted to bind with SARS-CoV-2 Receptor Binding Domain (RBD).