蛋白质折叠，一直是计算生物学中的一个核心难题，并对药物研发等领域产生着深远影响。 若把蛋白质折叠类比为视觉领域的生成模型，氨基酸序列相当于「提示词」，模型输出则是原子的三维坐标。 受此思维启发，研究人员构建了一个基于标准Transformer模块与自适应层的通用且强大的架构——SimpleFold。 论文地址：https://arxiv.org/abs/2509.18480 SimpleFold和AlphaFold2等经典的蛋白质折叠模型有哪些不同？ AlphaFold2、RoseTTAFold2通过融合复杂且高度专业化的架构，如三角更新、成对表示、多序列比对（MSA）。 这些设计往往是将我们对结构生成机制的已有理解「硬编码」到模型中，而不是让模型自己从数据中学习生成方式。 SimpleFold则提出了一种全新思路： 没有三角更新、成对表示，也不需要MSA，而是完全基于通用Transformer和流匹配（flow-matching），能够直接将蛋白质序列映射为完整的三维原子结 构（见图1）。 SimpleFold 首个基于Transformer模块的蛋白折叠模型 流匹配把生成视作一段随时间推进的旅程，用常微分 ...

Group 1 - The core viewpoint of the article emphasizes the strategic importance of AI as a key driver for high-quality development in China, as outlined in the recent government opinion document [1][3][10] - The document identifies six key actions and eight foundational supports to promote the dual empowerment of technology and application, aiming for deep integration of AI into various sectors including scientific research, industry, and public welfare [1][3] Group 2 - AI is positioned as a "key increment" for high-quality development, with its core value reflected in four dimensions: empowerment, burden reduction, quality improvement, and efficiency enhancement [3][10] - AI is expanding the cognitive boundaries of scientific research, acting as an accelerator for foundational studies, such as AlphaFold solving the protein folding problem [3] - The document highlights AI's role in reducing workload through automation, thereby creating better job opportunities and enhancing consumer satisfaction [3] - In manufacturing, AI has been shown to reduce equipment failure rates by 20%, while in education and healthcare, AI systems are customizing learning paths and assisting doctors, respectively [3] Group 3 - The document addresses the need for a "safety and controllability" principle, emphasizing the importance of preventing security risks associated with AI [6][10] - It outlines inherent risks of AI models, including their "black box" nature, which leads to challenges in understanding decision-making processes and vulnerabilities to adversarial attacks [6] - Ethical challenges are also highlighted, where biases in training data can amplify societal issues, potentially leading to the spread of negative sentiments [6] Group 4 - The document proposes a new governance framework that emphasizes multi-dimensional collaboration to ensure the safe development of AI [8][9] - It suggests a "four-in-one" collaborative governance system that includes improving legal frameworks, establishing a multi-faceted public safety system, creating a network governance system, and developing an intelligent emergency response system [8] - The document also emphasizes enhancing safety governance capabilities across four key areas: technical safety, ethical safety, application safety, and national security [9]