分子制造实验室研究所:加速、推进和民主化分子创新

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2024-02-17 DOI:10.1002/aaai.12154
Martin D. Burke, Scott E. Denmark, Ying Diao, Jiawei Han, Rachel Switzky, Huimin Zhao
{"title":"分子制造实验室研究所:加速、推进和民主化分子创新","authors":"Martin D. Burke,&nbsp;Scott E. Denmark,&nbsp;Ying Diao,&nbsp;Jiawei Han,&nbsp;Rachel Switzky,&nbsp;Huimin Zhao","doi":"10.1002/aaai.12154","DOIUrl":null,"url":null,"abstract":"<p>Many of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI-experts from the University of Illinois Urbana-Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI-enabled synthesis planning, (2) AI-enabled catalyst development, (3) AI-enabled molecule manufacturing, and (4) AI-enabled molecule discovery. The MMLI's new AI-enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use-inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI-enabled tools can help to make chemical synthesis accessible to nonexperts.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"45 1","pages":"117-123"},"PeriodicalIF":2.5000,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12154","citationCount":"0","resultStr":"{\"title\":\"Molecule Maker Lab Institute: Accelerating, advancing, and democratizing molecular innovation\",\"authors\":\"Martin D. Burke,&nbsp;Scott E. Denmark,&nbsp;Ying Diao,&nbsp;Jiawei Han,&nbsp;Rachel Switzky,&nbsp;Huimin Zhao\",\"doi\":\"10.1002/aaai.12154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Many of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI-experts from the University of Illinois Urbana-Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI-enabled synthesis planning, (2) AI-enabled catalyst development, (3) AI-enabled molecule manufacturing, and (4) AI-enabled molecule discovery. The MMLI's new AI-enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use-inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI-enabled tools can help to make chemical synthesis accessible to nonexperts.</p>\",\"PeriodicalId\":7854,\"journal\":{\"name\":\"Ai Magazine\",\"volume\":\"45 1\",\"pages\":\"117-123\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12154\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ai Magazine\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aaai.12154\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ai Magazine","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aaai.12154","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

当今社会面临的许多重大挑战都可能有有待发现的分子解决方案。然而,识别和制造此类分子的过程仍然十分缓慢,而且高度依赖专家。人工智能(AI)和合成有机化学领域的结合有可能有力地解决这两个局限性。分子制造实验室研究所(MMLI)汇集了来自伊利诺伊大学香槟分校(UIUC)、宾夕法尼亚州立大学和罗切斯特理工学院的化学家、工程师和人工智能专家,其目标是加速复杂有机分子的发现、合成和制造。先进的人工智能和机器学习(ML)方法主要应用于四个关键领域:(1) 人工智能合成规划,(2) 人工智能催化剂开发,(3) 人工智能分子制造,以及 (4) 人工智能分子发现。该研究所的新人工智能合成平台将化学和酶催化与文献挖掘和人工智能相结合,以预测制造具有理想生物和材料特性的新分子的最佳方法。MLI 正在改变化学合成,并产生受用途启发的人工智能进步。同时,该研究所也是培养下一代科学家的基地,这些科学家拥有化学和人工智能方面的综合专业知识。面向高中生和公众的宣传工作正在被用来展示人工智能工具如何帮助非专业人员进行化学合成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Molecule Maker Lab Institute: Accelerating, advancing, and democratizing molecular innovation

Many of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI-experts from the University of Illinois Urbana-Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI-enabled synthesis planning, (2) AI-enabled catalyst development, (3) AI-enabled molecule manufacturing, and (4) AI-enabled molecule discovery. The MMLI's new AI-enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use-inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI-enabled tools can help to make chemical synthesis accessible to nonexperts.

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
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
期刊最新文献
Issue Information AI fairness in practice: Paradigm, challenges, and prospects Toward the confident deployment of real-world reinforcement learning agents Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning Efficient and robust sequential decision making algorithms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1