Core Viewpoint - The 2025 Nobel Prize in Chemistry has been awarded to Susumu Kitagawa, Richard Robson, and Omar M. Yaghi for their contributions to the development of metal-organic frameworks [1] Group 1: Awardees Background - Susumu Kitagawa, born in 1951, is affiliated with Kyoto University and focuses on the fundamental research and application development of metal-organic framework materials [3] - Richard Robson, born in 1937, works at the University of Melbourne and has made significant contributions to the theoretical foundations of metal-organic frameworks [6] - Omar M. Yaghi is a professor at the University of California, Berkeley, known for major breakthroughs in the synthesis methods and practical applications of metal-organic frameworks [9] Group 2: Nobel Prize History - As of October 2024, the Nobel Prize in Chemistry has been awarded 116 times to 197 recipients, with 63 awards given to individuals, 25 shared by two, and 28 shared by three [12] - Notable statistics include 8 years where the award was not given, 9 years of delayed awards, and the recognition of 8 female laureates [12] - The youngest laureate was Jean Frédéric Joliot-Curie, who won at age 35 in 1935, while the oldest was John Goodenough, awarded at age 97 for his work on lithium batteries [12] Group 3: Recent Nobel Prize Winners - In 2024, half of the prize was awarded to David Baker, with the other half shared by Demis Hassabis and John Jumper for their contributions to protein design and structure prediction [13] - The 2023 prize was awarded to Mogi Bawendi, Louis Brus, and Alexei Ekimov for their discovery and synthesis of quantum dots [14] - In 2022, the award went to Carolyn Bertozzi, Morten Meldal, and Carolyn Bertozzi for their work in click chemistry and bioorthogonal chemistry [15]

2025年诺贝尔化学奖揭晓 三位科学家共同获奖 中新网10月8日电(记者 管娜)当地时间10月8日，瑞典皇家科学院决定将2025年诺贝尔化学奖授予北川进 (Susumu Kitagawa)、理查德·罗布森(Richard Robson)以及奥马尔·M·亚吉(Omar M. Yaghi)三位科学家，以 表彰其在金属有机骨架开发领域的贡献。获奖者将平分1100万瑞典克朗(约合836万元人民币)奖金。 "理科综合奖"：无交叉不化学 "化学是阿尔弗雷德·诺贝尔自身工作中最重要的科学。他的发明以及他采用的工业流程都是基于化学知 识。化学是诺贝尔遗嘱中提到的第二个获奖领域。"诺贝尔奖官网上对化学奖的介绍，足以看出该奖项 的重要地位。 自1901年以来，诺贝尔化学奖共颁发了116次，回顾其颁奖历程，历届诺贝尔化学奖得主中，不乏跨界 学者，许多人的获奖成就也并非出自传统的化学研究，而是涉及生物学、物理学等多重学科。因此，诺 贝尔化学奖也被调侃为"理科综合奖"。 2017年的诺贝尔化学奖颁给了在冷冻电子显微镜技术领域做出巨大贡献的三位生物物理学家，2018年的 诺贝尔化学奖颁给了在"进化控制"方面做出重要贡献的三位生物学家。 ...

（原标题：刚刚，2025年诺贝尔化学奖揭晓！） 瑞典皇家科学院当地时间10月8日宣布，将2025年诺贝尔化学奖授予北川进 （Susumu Kitagawa）、理 查德·罗布森（Richard Robson）和奥马尔·M·亚吉 （Omar M. Yaghi），以表彰他们"在金属有机框架领 域的发展"。 北川进 （Susumu Kitagawa） 理查德·罗布森（Richard Robson） 奥马尔·M·亚吉 （Omar M. Yaghi） 截至2024年10月，诺贝尔化学奖已经颁发116次，有197位获得者，其中63次由1人获得，25次由2人分 享，28次由3人共享。其中有8年因故停发；有9年延迟一年颁发；2位两次获奖；8位女性获奖；一对夫 妻获奖；一对母女获奖。 最年长的诺贝尔化学奖是美国科学家约翰·古迪纳夫，时年97岁。他被誉为"锂电池之父"，他的研究彻 底改变了手机、电脑以及电动汽车的充电方式。2023年6月，古迪纳夫去世，享年100岁。 近三年诺贝尔化学奖获奖情况： 2024年一半授予大卫·贝克，另一半共同授予德米斯·哈萨比斯和约翰·江珀，以分别表彰他们在蛋白质设 计和蛋白质结构预测领域的贡献。 20 ...

Core Viewpoint - The emergence of D-I-TASSER, a new protein structure prediction tool, demonstrates significant advancements in protein folding prediction, outperforming existing models like AlphaFold2 and AlphaFold3 in accuracy and coverage [3][8]. Group 1: D-I-TASSER Development and Performance - D-I-TASSER was developed by a collaborative research team and has shown superior performance in the CASP15 competition, excelling in both single-domain and multi-domain protein structure predictions [3][8]. - The tool successfully predicted structures for 19,512 proteins from the human proteome, achieving 81% domain coverage and 73% full-length sequence coverage, which is a notable improvement over AlphaFold2 [3][12][14]. - D-I-TASSER integrates deep learning with physical simulations, utilizing multiple sources of information to enhance prediction accuracy [8][14]. Group 2: Technical Innovations - The core innovation of D-I-TASSER lies in its hybrid approach, combining deep learning with physical modeling to refine protein structure predictions [8][17]. - The tool employs an upgraded DeepMSA2 for multi-sequence alignment, increasing information retrieval from metagenomic databases by 6.75 times [11]. - D-I-TASSER's modeling process includes a unique workflow of automatic domain cutting, independent prediction, and dynamic assembly, resulting in improved accuracy and reduced orientation errors [8][11]. Group 3: Challenges and Future Directions - Despite its impressive performance, D-I-TASSER faces challenges such as reduced prediction accuracy for orphan proteins and higher computational time compared to pure deep learning models [20]. - The research indicates that the ultimate solution to protein folding may lie in the deep synergy between data-driven methods and physical simulations [17][20]. - The D-I-TASSER model and its human protein structure prediction database have been made open-source, promoting further research and collaboration in the field [17].