CAREER: Merging Graph Theory and Automation for Chemical Synthesis

在化学系化学合成计划的支持下，密歇根大学的蒂莫西·切尔纳克正在建立一个将化学合成与数据科学相结合的计划。这一点很重要，因为发明未来的药物、聚合物、农用化学品和储能材料需要化学反应；然而，目前很难准确预测这些过程的结果。需要公开的化学反应数据，以及能够改进预测反应模型的算法和理论，以便在分子合成中应用，即使在目标复杂性很高的情况下也是如此。该项目利用最先进的自动化和机器人系统，使用高通量实验(HTE)策略执行数千个化学反应。然后，这些实验的数据将与机器学习、人工智能和其他形式的数据科学合作，目的是建立强有力的化学反应预测模型。为了促进更广泛的社区推进机器学习模型，将向公众发布数千个机器可读格式的化学反应数据点。瑟纳克博士和他的研究小组带头开展了一系列教育活动，重点是通过编程练习和免费软件的使用，向本科生和研究生介绍化学合成和数据科学的接口。幼儿也被包括在教育和推广计划中，通过适合年龄的活动，旨在通过玩井盘混色玩具为幼儿园儿童提供他们自己的高通量实验体验。该奖项支持的研究分为三个研究主题，均在化学合成和数据科学的界面上，如下：(1)识别在合成中具有高战略价值的新转化，(2)新反应类型的实验实现，以及(3)基于人工智能的逆向合成分析和随后的实验路线验证。在第一种情况下，使用基于图论的方法对可能的反应进行计算研究，以阐明未来发展的关键转变。例如，邻接矩阵映射技术将应用于萜类、生物碱和药物的全合成，以揭示尚不存在但仍可能成为合成化学工具箱中有效补充的化学转化类型。在第二个推力中，将结合高通量实验和机器学习方法，在实验中开发理论上合理的反应。应用集中在从胺和羧酸构建块中形成碳-氧键。这些反应方法的应用将得到小型化高通量实验的整体技术的进步的补充，无论是在硬件和软件层面，还是在机器学习算法的开发方面。最后一个主题集中于复杂目标分子的多步合成，如药物和代表性生物碱(例如，Stemo酰胺和钩吻碱)，其中化学反应序列通过逆合成算法连接在一起。总体而言，这项研究预计将促进化学合成和数据科学之间的基础知识和教育。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With the support of the Chemical Synthesis Program in the Division of Chemistry, Timothy Cernak of the University of Michigan is building a program that merges chemical synthesis with data science. This is important because chemical reactions are required to invent the medicines, polymers, agrochemicals, and energy storage materials of the future; however, at the present time, it is difficult to predict the outcome of such processes with accuracy. Publicly available chemical reaction data are needed, alongside algorithms and theories that can improve predictive reaction models toward applications in molecular synthesis even at high levels of target complexity. This project leverages state-of-the-art automated and robotic systems to perform thousands of chemical reactions using the strategy of high-throughput experimentation (HTE). Data from these experiments will then be partnered with machine learning, artificial intelligence, and other forms of data science with the aim of building robust predictive models of chemical reactivity. To facilitate the advancement of machine learning models by the wider community, thousands of chemical reaction data points in a machine-readable format will be released to the public domain. The broader impacts of the award are extended by a range of educational activities being spearheaded by Dr. Cernak and his research group that focus on introducing undergraduate and graduate students to the interface of chemical synthesis and data science through coding exercises and the use of freely available software. Young children are also included in the educational and outreach plans with age-appropriate activities designed to provide kindergartners with their own experiences of high-throughput experimentation through play with a well plate color-mixing toy.The research supported by this award is divided across three investigational themes, all at the interface of chemical synthesis and data science, as follows: (1) identification of new transformations with the potential for high strategic value in synthesis, (2) experimental realization of novel reaction types, and (3) AI-based retrosynthetic analysis and subsequent experimental route validation. In the first case, conceivable reactions are computationally studied using graph theory-based methods to illuminate key transformations for future development. For instance, adjacency matrix mapping techniques will be applied to total syntheses of terpenes, alkaloids, and pharmaceuticals to reveal chemical transformation types that do not yet exist but that could nonetheless become impactful additions to the synthetic chemistry toolbox. In the second thrust, theoretically sound reactions will be experimentally developed using a merger of high-throughput experimentation and machine learning methods. Applications center on the formation of carbon–oxygen bonds from amine and carboxylic acid building blocks. These reaction method applications are to be complemented by the advancement of overall techniques for miniaturized high-throughput experimentation, both at the hardware and software level, as well as in the development of machine learning algorithms. The final theme focuses on multistep synthesis of complex target molecules such as pharmaceuticals and representative alkaloids (e.g., stemoamide and gelsemine), where sequences of chemical reactions are stitched together via retrosynthetic algorithms. Collectively, this research is anticipated to advance basic knowledge and education at the interface of chemical synthesis and data science.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.