CAREER: Merging Graph Theory and Automation for Chemical Synthesis

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.