Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction

Xiao, W.; Zhang, Y.; Gong, C.; Zhang, H.; Ma, W.; Liu, Z.; Chen, X.; Guan, J.; Wang, L.

doi:10.64898/2026.02.05.703733

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bioRxiv · 10.64898/2026.02.05.703733

Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction

Xiao, W.·Zhang, Y.·Gong, C.·Zhang, H.·Ma, W.·Liu, Z.·Chen, X.·Guan, J.·Wang, L.

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Abstract

We introduce Protenix-v1 (PX-v1), the first fully 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 of Protenix-v1, demonstrating that increasing the sampling budget yields consistent improvements in prediction quality--a behavior previously observed in AlphaFold3 and largely absent from prior 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 limitations in existing benchmarking practices and provide updated evaluation tools and year-stratified benchmarks to support more reliable and transparent assessment. Collectively, these contributions provide a robust foundation for the Protenix series and the broader field.

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Xiao, W., Zhang, Y., Gong, C., Zhang, H., Ma, W., Liu, Z., Chen, X., Guan, J., Wang, L.. 2026-02-09. Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction. https://doi.org/10.64898/2026.02.05.703733

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