CCI Phase I: NSF Center for Computer Assisted Synthesis

CCI 第一阶段：NSF 计算机辅助合成中心

• 批准号: 1925607
• 负责人: Olaf Wiest
• 金额: $ 180万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925607&HistoricalAwards=false
• 关键词: CCI; Phase; NSF; Center; Computer

The NSF Center for Computer Assisted Synthesis (C-CAS) is supported by the Centers for Chemical Innovation (CCI) Program of the Division of Chemistry. This Phase I Center is led by Olaf Wiest of the University of Notre Dame. Other team members include Nitesh Chawla, also of the University of Notre Dame, Abigail Doyle of Princeton University, Robert Paton of Colorado State University, Richmond Sarpong of the University of California, Berkeley, and Matthew Sigman of the University of Utah. The goal of C-CAS is to combine data science, machine learning, artificial intelligence, chemical reactions and selectivity optimization, computational chemistry, and organic synthesis to transform how the synthesis of complex organic molecules is planned and executed. This center will transform chemical synthesis and increase the economic and societal competitiveness of pharmaceutical, chemical, and technology industries within the US. The research efforts are enhanced through multiple industrial partnerships. As a result, a new generation of interdisciplinary teams of "data chemists" and machine learning scholars will be trained to address the challenges and demands of modern synthetic chemistry. A network of research, networking and professional development opportunities will be established to bridge the gap between disciplines, drawing students from a broad range of backgrounds including students with disabilities. Social and mass media will be utilized to engage the general public. The vision of C-CAS is to create a comprehensive platform for computationally planning and optimizing synthetic pathways, predicting reaction performance, and addressing the selectivity (e.g., chemo-, regio, stereo-) challenges inherent in complex molecule synthesis. This center has three thrusts. The first thrust is to use representation learning to unify heterogeneous data from a variety of sources, including unbiased, dense, and "clean" microscopic and macroscopic data from the literature, patents, high-throughput experimentation, electronic laboratory notebooks and high-throughput computation. The second thrust is to exploit this unified data representation to address the "over-the arrow" problem of chemical reaction optimization that is the rate-limiting step in most syntheses, through active and transfer learning that will yield interpretable and explainable statistical and machine learning models. The third thrust is to demonstrate the combined use of heterogeneous data and optimization algorithms with existing synthesis planning programs to score possible synthetic routes and provide synthetic chemists with go/no-go decisions in the synthesis of a complex molecule. C-CAS provides quantitative training to a new generation of "data chemists" and to machine learning researchers, thereby equipping them for participation in interdisciplinary science, including industry-academic collaborations. The Center delivers programming such as online and in-person workshops, networking, and formulation and sharing of best practices in this rapidly evolving field that enhances the interdisciplinary experience and societal impact. The data science and computational portion in C-CAS provides non-traditional research and networking opportunities that are particularly suitable to students with disabilities and underrepresented minority students. C-CAS engages non-scientists in discussions of the integration of chemistry and machine learning through mass- and social media as well as in-person communications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF计算机辅助合成中心（C-CAS）由化学部的化学创新中心（CCI）计划提供支持。第一阶段中心由圣母大学的奥拉夫·维斯特领导。其他团队成员包括圣母大学的Nitesh Chawla，普林斯顿大学的Abigail Doyle，科罗拉多州立大学的Robert Paton，加州大学伯克利分校的里士满Sarpong和犹他州大学的Matthew Sigman。C-CAS的目标是将数据科学、机器学习、人工智能、化学反应和选择性优化、计算化学和有机合成相结合，以改变复杂有机分子合成的规划和执行方式。该中心将改变化学合成，提高美国制药，化学和技术行业的经济和社会竞争力。研究工作通过多种工业伙伴关系得到加强。因此，新一代的“数据化学家”和机器学习学者的跨学科团队将接受培训，以应对现代合成化学的挑战和需求。将建立一个研究、联网和专业发展机会网络，以弥合学科之间的差距，吸引来自广泛背景的学生，包括残疾学生。 将利用社交媒体和大众媒体让公众参与。C-CAS的愿景是创建一个综合平台，用于计算规划和优化合成途径，预测反应性能，并解决选择性（例如，化学、区域、立体）复杂分子合成中固有的挑战。这个中心有三个推力。 第一个重点是使用表示学习来统一来自各种来源的异构数据，包括来自文献、专利、高通量实验、电子实验室笔记本和高通量计算的无偏、密集和“干净”的微观和宏观数据。 第二个重点是利用这种统一的数据表示来解决化学反应优化的“过箭头”问题，这是大多数合成中的限速步骤，通过主动和迁移学习，将产生可解释和可解释的统计和机器学习模型。 第三个重点是展示异构数据和优化算法与现有的合成规划程序的结合使用，以评分可能的合成路线，并提供合成化学家与去/不去的决定，在合成一个复杂的分子。C-CAS为新一代“数据化学家”和机器学习研究人员提供定量培训，从而使他们能够参与跨学科科学，包括行业-学术合作。该中心提供编程，如在线和面对面的研讨会，网络，并制定和分享最佳实践，在这个快速发展的领域，增强跨学科的经验和社会影响。C-CAS的数据科学和计算部分提供了非传统的研究和网络机会，特别适合残疾学生和代表性不足的少数民族学生。C-CAS通过大众和社交媒体以及面对面交流，让非科学家参与化学和机器学习整合的讨论。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Rearrangements of the Chrysanthenol Core: Application to a Formal Synthesis of Xishacorene B.

• DOI: 10.1021/jacs.1c10804
• 发表时间: 2021-12-08
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Jones, Kerry E.;Park, Bohyun;Doering, Nicolle A.;Baik, Mu-Hyun;Sarpong, Richmond
• 通讯作者: Sarpong, Richmond

Auto-QChem: an automated workflow for the generation and storage of DFT calculations for organic molecules

Auto-QChem：用于生成和存储有机分子 DFT 计算的自动化工作流程

• DOI: 10.1039/d2re00030j
• 发表时间: 2022
• 期刊: Reaction Chemistry & Engineering
• 影响因子: 3.9
• 作者: Żurański, Andrzej M.;Wang, Jason Y.;Shields, Benjamin J.;Doyle, Abigail G.
• 通讯作者: Doyle, Abigail G.

Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources.

• DOI: 10.1021/jacs.1c12203
• 发表时间: 2022-01-19
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Kariofillis SK;Jiang S;Żurański AM;Gandhi SS;Martinez Alvarado JI;Doyle AG
• 通讯作者: Doyle AG

Impact of Phosphine Featurization Methods in Process Development

磷化氢特征化方法对工艺开发的影响

• DOI: 10.1021/acs.oprd.1c00357
• 发表时间: 2022
• 期刊: Organic Process Research & Development
• 影响因子: 3.4
• 作者: Crawford, Jennifer M.;Gensch, Tobias;Sigman, Matthew S.;Elward, Jennifer M.;Steves, Janelle E.
• 通讯作者: Steves, Janelle E.

Bayesian reaction optimization as a tool for chemical synthesis

• DOI: 10.1038/s41586-021-03213-y
• 发表时间: 2021-02-04
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Shields, Benjamin J.;Stevens, Jason;Doyle, Abigail G.
• 通讯作者: Doyle, Abigail G.