AI tool helps optimize antibody medicines

Machine learning points out why antibodies fail to stay on target, binding to molecules that aren't markers of disease-and suggests better designs. Study: Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning Antibodies fight disease by binding specific molecules called antigens on disease-causing agents-such as the spike protein on the virus that causes COVID-19. Once bound, the antibody either directly inactivates the harmful viruses or cells or signals the body's immune cells to do so. Unfortunately, antibodies designed to bind their specific antigens very strongly and quickly can also bind non-antigen molecules, which removes the antibodies before they target a disease. Such antibodies are also prone to binding with other antibodies of the same type and, in the process, forming thick solutions that don't flow easily through the needles that deliver antibody drugs. "The ideal antibodies should do three things at once: bind tightly to what they are supposed to, repel each other and ignore other things in the body,” Tessier said. An antibody that doesn't check all three boxes is unlikely to become a successful drug, but many clinical-stage antibodies can't.