NSF Center for Computer-Assisted Synthesis

NSF 计算机辅助合成中心

• 批准号: 2202693
• 负责人: Olaf Wiest
• 金额: $ 2000万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2202693&HistoricalAwards=false
• 关键词: NSF; Center; Computer; Assisted; Synthesis

The NSF Center for Computer Assisted Synthesis (CCAS) is a nexus of collaboration, innovation, and education that brings together data science and chemical synthesis. The highly interdisciplinary CCAS team, composed of synthetic organic chemists, computational chemists, and computer scientists, is developing data science tools and computational workflows that will likely shape the future of synthetic chemistry and the fields it enables, such as medicine, materials science, and energy research. This site’s impacts are being further amplified by an extensive network of academic, industrial and non-profit partners and research centers, and its data chemistry tools are being shared with the research community through open-source clearinghouses. All of this provides CCAS with a unique opportunity to develop, exchange, and evaluate ideas in the field of data chemistry, and its shared tools and training will empower students, practicing chemists, and the chemical industry to effectively apply data science to their own chemical research.Led by organic chemists at every stage, CCAS focuses on use-inspired data science research that drives the development of new data types and machine learning (ML) methods that enable the discovery of novel reactions and yield new scientific insights. The four scientific thrusts include (i) developing effective ML tools for optimizing chemical reactions, (ii) gaining mechanistic understanding through interpretable statistical models and electronic structure calculations, (iii) predicting reaction outcomes to anticipate and discover new reactivity and (iv) integrating these tools for the efficient planning and execution of multistep syntheses of complex molecules. To accomplish these goals, three themes are interwoven into each of the thrusts: (a) new structured data types that are amenable to high-throughput experimentation and predictive models from the ground up, going beyond the information from commonly used databases, (b) molecular and reaction representations that bridge descriptor-based and structure-based deep learning paradigms, and (c) algorithms specifically designed for the low data regimes prevalent throughout chemistry. Through these integrated research themes and thrusts, CCAS constructs and shares data chemistry platforms that are expected to enable chemists to tackle ambitious challenges that the field is currently under-equipped to pursue. The data chemistry platform also will open up new opportunities in undergraduate and graduate education, and through partnerships with the Data Chemists Network and research opportunities for chemists with disabilities, CCAS seeks to broaden the participation of researchers from underrepresented groups.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（CCAS）是一个合作，创新和教育的纽带，将数据科学和化学合成结合在一起。由合成有机化学家，计算化学家和计算机科学家组成的高度跨学科的CCAS团队正在开发数据科学工具和计算工作流程，这些工具和工作流程可能会塑造合成化学的未来及其所支持的领域，如医学，材料科学和能源研究。该网站的影响力正在通过广泛的学术，工业和非营利合作伙伴和研究中心网络进一步扩大，其数据化学工具正在通过开源信息交换所与研究社区共享。所有这些都为CCAS提供了一个独特的机会来发展、交流和评估数据化学领域的想法，其共享的工具和培训将使学生、执业化学家和化学行业能够有效地将数据科学应用于自己的化学研究，在每个阶段都由有机化学家带领CCAS专注于使用启发的数据科学研究，推动新数据类型和机器学习（ML）方法的开发，从而发现新的反应并产生新的科学见解。这四个科学方向包括：（i）开发有效的机器学习工具来优化化学反应;（ii）通过可解释的统计模型和电子结构计算获得机械理解;（iii）预测反应结果以预测和发现新的反应性;（iv）整合这些工具，以有效规划和执行复杂分子的多步合成。为了实现这些目标，三个主题交织在每一个重点中：（a）新的结构化数据类型，其适合于高通量实验和从头开始的预测模型，超越了来自常用数据库的信息，（B）桥接基于生物反应器和基于结构的深度学习范例的分子和反应表示，和（c）专门为整个化学中普遍存在的低数据状态设计的算法。通过这些综合研究主题和重点，CCAS构建和共享数据化学平台，预计将使化学家能够应对该领域目前装备不足的雄心勃勃的挑战。数据化学平台还将为本科生和研究生教育开辟新的机会，并通过与数据化学家网络的合作伙伴关系和残疾化学家的研究机会，CCAS旨在扩大来自代表性不足群体的研究人员的参与。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进行评估，被认为值得支持的搜索.

项目成果

Character As Pixels: A Controllable Prompt Adversarial Attacking Framework for Black-Box Text Guided Image Generation Models

• DOI: 10.24963/ijcai.2023/109
• 发表时间: 2023-08
• 作者: Ziyi Kou;Shichao Pei;Yijun Tian;Xiangliang Zhang

Uncertainty-Aware Yield Prediction with Multimodal Molecular Features

• DOI: 10.1609/aaai.v38i8.28668
• 发表时间: 2024-03
• 期刊: Renewable & Sustainable Energy Reviews
• 影响因子: 15.9
• 作者: Jiayuan Chen;Kehan Guo;Zhen Liu;O. Isayev;Xiangliang Zhang

Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes

• DOI: 10.1021/acscatal.4c00650
• 发表时间: 2024-03
• 期刊: ACS Catalysis
• 影响因子: 12.9
• 作者: Natalie P. Romer;Daniel S Min;Jason Y. Wang;R. Walroth;Kyle A. Mack;Lauren E. Sirois;F. Gosselin;Daniel Zell;A. Doyle;M. Sigman

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

结合分子量子力学建模和机器学习来加速反应筛选和发现

• DOI: 10.1002/chem.202301957
• 发表时间: 2023
• 期刊: Chemistry – A European Journal
• 作者: Casetti, Nicholas;Alfonso‐Ramos, Javier E.;Coley, Connor W.;Stuyver, Thijs
• 通讯作者: Stuyver, Thijs

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters

自下而上的宏观测量的原子描述：哈米特电子参数的计算基准

• DOI: 10.1021/acsphyschemau.3c00045
• 发表时间: 2024
• 期刊: ACS Physical Chemistry Au
• 作者: Luchini, Guilian;Paton, Robert S.
• 通讯作者: Paton, Robert S.