Αlpha¹ Review in Progress

We introduce Protenix-v1 (PX-v1), the first open-source structure prediction model to attain superior performance to AlphaFold3 while strictly adhering to the same training data cutoff, model size, and inference budget. Beyond standard evaluations, we highlight the effectiveness of inference-time scaling behavior, demonstrating that increasing the sampling budget yields consistent improvements in prediction quality--a behavior previously seen in AlphaFold3 but not in other open-source models. In addition to improved accuracy, Protenix-v1 incorporates key capabilities including protein template integration and RNA MSA support. Furthermore, to better support real-world applications such as drug discovery, we additionally release Protenix-v1-20250630, a variant trained on a larger dataset (cutoff: June 30, 2025), delivering further improved prediction accuracy. Finally, we identify the limitations of current benchmarking tools and we provide updated evaluation tools and year-stratified benchmarks to facilitate more reliable and transparent assessment within the community. Collectively, these contributions provide a robust foundation for the Protenix series and the broader field.