On Nov. 25, 2025, a team of Massachusetts Institute of Technology scientists reported that AI model BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.

Building upon Boltz-2, an open-source biomolecular structure prediction model predicting protein binding affinity that made waves over the summer, BoltzGen (officially released October 26) is the first model of its kind to go a step further by generating novel protein binders that are ready to enter the drug discovery pipeline.

Three key innovations make this possible: first, BoltzGen’s ability to carry out a variety of tasks, unifying protein design and structure prediction while maintaining state-of-the-art performance. Next, BoltzGen’s built-in constraints are designed with feedback from wetlab collaborators to ensure the model creates functional proteins that don’t defy the laws of physics or chemistry. Lastly, a rigorous evaluation process tests the model on “undruggable” disease targets, pushing the limits of BoltzGen’s binder generation capabilities.

Most models used in industry or academia are capable of either structure prediction or protein design. Moreover, they’re limited to generating certain types of proteins that bind successfully to easy “targets.” Much like students responding to a test question that looks like their homework, as long as the training data looks similar to the target during binder design, the models often work. But existing methods are nearly always evaluated on targets for which structures with binders already exist, and end up faltering in performance when used on more challenging targets.

The BoltzGen researchers went out of their way to test BoltzGen on 26 targets, ranging from therapeutically relevant cases to ones explicitly chosen for their dissimilarity to the training data. This comprehensive validation process, which took place in eight wetlabs across academia and industry, demonstrates the model’s breadth and potential for breakthrough drug development.