Core Insights - The article discusses the advancements in AI protein design, particularly through the introduction of the PXDesign method by ByteDance's Seed team, which significantly enhances the efficiency and success rates of protein design tasks [1][3][10]. Summary by Sections Introduction to PXDesign - PXDesign is a scalable protein design method that allows for the generation of hundreds of high-quality candidate proteins within 24 hours, achieving a generation efficiency approximately 10 times higher than mainstream methods [3][10]. - The method has demonstrated a wet lab success rate of 20%–73% across multiple targets, surpassing the success rates of existing models like DeepMind's AlphaProteo, which ranges from 9% to 33% [3][10]. Background and Significance - Proteins are fundamental to life processes, and recent Nobel Prizes in Chemistry highlight the importance of both protein structure prediction and design [6]. - The challenge lies not only in predicting structures but also in reverse designing proteins based on functional requirements, which is crucial for developing new therapies for diseases like cancer and infections [7][8]. Methodology of PXDesign - PXDesign employs a "generation + filtering" approach, where a large number of candidate designs are generated quickly, followed by a filtering process to identify the most promising candidates [13][21]. - The team explored two main technical routes: Hallucination and Diffusion, with PXDesign-d (Diffusion) showing superior performance in generating high-quality, diverse structures [15][16]. Advantages of PXDesign - PXDesign-d utilizes a DiT network structure, allowing for efficient training on larger datasets, which enhances generation speed and quality compared to other methods [17]. - The filtering process uses structural prediction models to select the most viable candidates, with Protenix outperforming AlphaFold 2 in accuracy and efficiency [25][26]. Tools and Services - The Protenix team has developed the PXDesign Server, a user-friendly web service that allows researchers to design and evaluate binder candidates without needing complex setups [28][29]. - The server offers two modes: Preview for quick debugging and Extended for in-depth research, significantly reducing the design cycle compared to traditional methods [30][32]. Evaluation Standards - To address the lack of unified evaluation standards in the field, the Protenix team introduced PXDesignBench, a comprehensive evaluation toolbox that integrates various assessment metrics and processes [32]. Industry Context - Other tech giants like Microsoft and Apple are also making strides in the biological field, indicating a growing trend of AI applications in biotechnology and pharmaceuticals [33].