AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

• URL: http://arxiv.org/abs/2507.08920v2
• Date: Tue, 29 Jul 2025 05:17:40 GMT
• Title: AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
• Authors: Changze Lv, Jiang Zhou, Siyu Long, Lihao Wang, Jiangtao Feng, Dongyu Xue, Yu Pei, Hao Wang, Zherui Zhang, Yuchen Cai, Zhiqiang Gao, Ziyuan Ma, Jiakai Hu, Chaochen Gao, Jingjing Gong, Yuxuan Song, Shuyi Zhang, Xiaoqing Zheng, Deyi Xiong, Lei Bai, Wanli Ouyang, Ya-Qin Zhang, Wei-Ying Ma, Bowen Zhou, Hao Zhou,
• Abstract summary: We introduce AMix-1, a powerful protein foundation model built on Flow Bayesian Networks.<n>AMix-1 is empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm.<n>Building on this foundation, we devise a multiple sequence alignment (MSA)-based in-context learning strategy to unify protein design into a general framework.
• Score: 92.51919604882984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm. To guarantee robust scalability, we establish a predictive scaling law and reveal the progressive emergence of structural understanding via loss perspective, culminating in a strong 1.7-billion model. Building on this foundation, we devise a multiple sequence alignment (MSA)-based in-context learning strategy to unify protein design into a general framework, where AMix-1 recognizes deep evolutionary signals among MSAs and consistently generates structurally and functionally coherent proteins. This framework enables the successful design of a dramatically improved AmeR variant with an up to $50\times$ activity increase over its wild type. Pushing the boundaries of protein engineering, we further empower AMix-1 with an evolutionary test-time scaling algorithm for in silico directed evolution that delivers substantial, scalable performance gains as verification budgets are intensified, laying the groundwork for next-generation lab-in-the-loop protein design.

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