A newly reported generative AI model is designed to predict protein–protein interactions at a scale intended to accelerate therapeutic discovery and protein engineering. The work is positioned around the central idea that many drugs—including antibody therapies and replacement protein strategies—work by modulating how proteins interact. For drug developers, the announcement signals continued investment in AI methods that can map interaction networks and support target selection, antibody development and rational protein design. The article suggests the model can produce predictions useful for engineering interaction properties relevant to disease. Given how often interaction assays sit upstream of lead optimization, any improvement in predictive accuracy can reduce experimental iteration cycles in discovery programs.