Martin D. Burke, Scott E. Denmark, Ying Diao, Jiawei Han, Rachel Switzky, Huimin Zhao
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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, Scott E. Denmark, Ying Diao, Jiawei Han, Rachel Switzky, 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. 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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.
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.