A new generative AI model was introduced to predict protein–protein interactions at the atomic scale, targeting one of the most computationally intensive steps in structural biology and therapeutic discovery. The work highlighted the centrality of protein interactions for diseases and for modalities such as antibodies, where binding interfaces often determine efficacy. The report positions the model as a potential accelerator for predicting how proteins meet and interact, which could support both target validation and protein engineering—capabilities that are increasingly relevant as biopharma adopts AI-driven design pipelines. In practice, atomic-scale prediction requires high fidelity to be useful for binding hypotheses and downstream experimental planning. If the model can generalize beyond benchmark systems, it may reduce the time from target selection to interaction hypotheses in drug programs, especially for teams working on antibody engineering and protein therapeutics.