Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design
Yuxuan ZhangiD, Chengyue GongiD, Jinyuan SuniD, Jiaqi GuaniD, Milong ReniD, Song Xue, Hanyu ZhangiD, Wenzhi MaiD, Zhenyu Liu, Xinshi CheniD, Wenzhi XiaoiD
Abstract
Advances in biomolecular modeling have broadened the range of problems addressable by structure prediction and design models. Here, we present results from Protenix-v2, a system spanning high-accuracy structure prediction and biomolecular design. On the structure prediction side, Protenix-v2 achieves antibody-antigen success rates with up to 13-point gains over Protenix-v1, while 5-seed performance surpasses previous 1000-seed results. On the design side, Protenix-v2 demonstrates a 100% target-level success rate in novelty-controlled VHH-Fc campaigns, reaching hit rates up to 48%. Crucially, the model enables hit discovery on difficult GPCR targets with hit rates of 16%-88% (VHH-Fc) and up to 50% (mAb) under 16-30 testing budgets per target. Resulting hits show high developability and diversity. Beyond antibody tasks, we report improved ligand-related plausibility and successful cross-variant SARS-CoV-2 spike RBD mini-binder design. These results establish Protenix-v2 as a robust and powerful model for accelerated drug discovery.
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