PXDesign: Fast, Modular, and Accurate De Novo Design of Protein Binders

摘要

PXDesign achieves nanomolar binder hit rates of 20–73% across five of six diverse protein targets, surpassing prior methods such as AlphaProteo. This experimental success rate is enabled by advances in both binder generation and filtering. We develop both a diffusion-based generative model (PXDesign-d) and a hallucination-based approach (PXDesign-h), each showing strong in silico performance that outperforms existing models. Beyond generation, we systematically analyze confidence-based filtering and ranking strategies from multiple structure predictors, comparing their accuracy, efficiency, and complementarity on datasets spanning de novo binders and mutagenesis. Finally, we validate the full design process experimentally, achieving high hit rates and multiple nanomolar binders. To support future work and community use, we release a unified benchmarking framework at https://github.com/bytedance/PXDesignBench, provide public access to PXDesign via a webserver at https://protenix-server.com, and share all designed binder sequences at https://protenix.github.io/pxdesign.

作者

Milong Ren, Jinyuan Sun, Jiaqi Guan, Cong Liu, Chengyue Gong, Yuzhe Wang, Lan Wang, Qixu Cai, Xinshi Chen, Wenzhi Xiao, Protenix Team

期刊/会议

arXiv