Advancing AI for biology: Teaching models to design and characterize antibodies

Monoclonal antibodies are one of the workhorses of biopharmaceutical development, with over 100 FDA-approved drugs and well-established manufacturing, regulatory, and clinical-development pathways. Yet conventional antibody discovery remains hampered by mounting costs and long timelines, typically six to twelve months to get from a target to a lead candidate. By designing and characterizing therapeutic antibodies computationally, AI promises to make development cheaper, faster, and more flexible. But scientific questions abound. Development of an antibody-based drug hinges on three factors: the best binding site on the target, which candidates bind to it most tightly, and whether any of them can survive manufacturing and the clinic. For each, the field has predictive models that do well on familiar targets and assays…