AMix-2: A Protein-Text Cross-Modal Foundation Model Released by Shanghai AI Laboratory

Shanghai AI Laboratory has launched AMix-2, a new-generation protein-text foundation model. It enables native protein understanding and generative design based on a diffusion-based large language model, marking a major breakthrough in the deep integration of AI and life sciences. The model has cross-modal capabilities, which can establish connections between protein sequences, structures, and natural language descriptions, providing new tools for protein research and design.

Today, with the integration of AI and life sciences, protein research is undergoing a transformation. Traditional protein engineering relies on extensive experimental trial-and-error and expert experience, while the introduction of deep learning technology has completely changed the landscape. AMix-2 is not only a protein sequence prediction tool but also a cross-modal foundation model that can simultaneously understand protein structures and text descriptions.

The core innovation of AMix-2 lies in applying diffusion models (a technology in the field of image generation) to protein sequence generation and understanding. Unlike traditional autoregressive architectures, diffusion models generate sequences through gradual denoising, offering stronger controllability and diversity. Trained on large-scale protein-text aligned data, the model can establish deep connections between protein sequences, structural features, and natural language descriptions, supporting the generation of corresponding protein sequences using natural language descriptions of functions.

Native protein understanding refers to the model's deep grasp of the essential properties of proteins, rather than simple pattern matching. AMix-2 can capture long-range interactions in sequences, folding dynamics, and spatial relationships of functional sites, bringing three aspects of value:

• Improved functional prediction accuracy: Predicting catalytic activity, binding affinity, etc., based on sequences
• Mutation effect evaluation: Quickly assessing the impact of point mutations on stability and function
• Novel protein design: Generating artificial proteins that do not exist in nature but have expected functions

The application prospects of AMix-2 cover multiple fields: Accelerated drug development: Generate protein molecules with specific binding capabilities to shorten the development cycle of antibody drugs Industrial enzyme optimization: Design industrial enzymes that remain active in extreme environments to reduce production costs Empowerment of synthetic biology: Provide customized enzyme components to promote the production of sustainable chemicals Basic research tool: Serve as a computational platform for studying structure-function relationships and verifying theoretical hypotheses

Although AMix-2 has made breakthroughs, it still faces challenges: Experimental verification bottleneck: Computationally generated proteins need wet-lab experiments to verify their functions, which is a rate-limiting step Dynamic conformation capture: It is difficult to fully capture the ensemble of dynamic conformations of proteins under physiological conditions Safety considerations: Artificially designed proteins may have unforeseen biological activities, so a safety assessment framework needs to be established In the future, with the development of experimental automation and multi-modal data fusion, such models will become more core in protein engineering and enable precise design.

The release of AMix-2 marks China's important position in the AI-driven life science competition. This open-source project provides a powerful tool for academic research and opens a new door for the industry to explore protein design. It is a benchmark project worthy of in-depth study by AI+biotechnology developers.