第76届Mosbacher学术讨论会：结构生物学和蛋白质设计中人工智能驱动的进化。

IF 2.4 4区生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY

Biological Chemistry Pub Date : 2025-09-30 DOI:10.1515/hsz-2025-0184

Birte Höcker, Ina Koch, Janosch Hennig

{"title":"第76届Mosbacher学术讨论会：结构生物学和蛋白质设计中人工智能驱动的进化。","authors":"Birte Höcker, Ina Koch, Janosch Hennig","doi":"10.1515/hsz-2025-0184","DOIUrl":null,"url":null,"abstract":"The 76th Mosbacher Kolloquium focused on recent advances in machine learning applications for structural biology and protein design. It covered topics spanning artificial intelligence-driven protein structure prediction, integrative modeling, generative protein design, and general applications in life sciences. With strong participation, high-caliber talks, and a clear focus on the integration of AI in biomolecular research, the meeting underscored the transformative role of machine learning in molecular biosciences and provided a vibrant platform for knowledge exchange across disciplines and generations.","PeriodicalId":8885,"journal":{"name":"Biological Chemistry","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The 76th Mosbacher Colloquium: AI-driven (r)evolution in structural biology and protein design.\",\"authors\":\"Birte Höcker, Ina Koch, Janosch Hennig\",\"doi\":\"10.1515/hsz-2025-0184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The 76th Mosbacher Kolloquium focused on recent advances in machine learning applications for structural biology and protein design. It covered topics spanning artificial intelligence-driven protein structure prediction, integrative modeling, generative protein design, and general applications in life sciences. With strong participation, high-caliber talks, and a clear focus on the integration of AI in biomolecular research, the meeting underscored the transformative role of machine learning in molecular biosciences and provided a vibrant platform for knowledge exchange across disciplines and generations.\",\"PeriodicalId\":8885,\"journal\":{\"name\":\"Biological Chemistry\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biological Chemistry\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1515/hsz-2025-0184\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological Chemistry","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1515/hsz-2025-0184","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

第76届Mosbacher Kolloquium会议重点关注机器学习在结构生物学和蛋白质设计中的应用的最新进展。它涵盖了人工智能驱动的蛋白质结构预测、综合建模、生成蛋白质设计和生命科学中的一般应用等主题。会议以高参与度、高水平的演讲和对人工智能在生物分子研究中的整合的明确关注为重点，强调了机器学习在分子生物科学中的变革性作用，并为跨学科和跨代的知识交流提供了一个充满活力的平台。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The 76th Mosbacher Colloquium: AI-driven (r)evolution in structural biology and protein design.

The 76th Mosbacher Kolloquium focused on recent advances in machine learning applications for structural biology and protein design. It covered topics spanning artificial intelligence-driven protein structure prediction, integrative modeling, generative protein design, and general applications in life sciences. With strong participation, high-caliber talks, and a clear focus on the integration of AI in biomolecular research, the meeting underscored the transformative role of machine learning in molecular biosciences and provided a vibrant platform for knowledge exchange across disciplines and generations.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Biological Chemistry 生物-生化与分子生物学

CiteScore

7.20

自引率

0.00%

发文量

审稿时长

4-8 weeks

期刊介绍： Biological Chemistry keeps you up-to-date with all new developments in the molecular life sciences. In addition to original research reports, authoritative reviews written by leading researchers in the field keep you informed about the latest advances in the molecular life sciences. Rapid, yet rigorous reviewing ensures fast access to recent research results of exceptional significance in the biological sciences. Papers are published in a "Just Accepted" format within approx.72 hours of acceptance.