CAREER: Development of Novel Domain-Tailored Machine Learning Tools for Organic Reaction Development and Discovery

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WIth support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Connor W. Coley of the Massachusetts Institute of Technology will establish new algorithms, computational approaches, and educational tools for the data-driven modeling of synthetic organic reactions to advance their development and discovery. Organic synthesis enables the creation of functional molecules that have impact in wide range of fields, including medicinal chemistry, chemical probe technology, polymer science, agrochemical science, catalyst development, and organic electronics. Dr. Coley and their team will build novel computer-aided synthetic chemistry tools to make the synthesis of new molecules more predictable and robust, augmenting and enhancing the intuition of expert practitioners of synthetic chemistry. This goal will be pursued through the close integration of experimental data, theoretical chemistry knowledge, and machine learning modelling. The new capabilities generated through this research will tangibly benefit the cost and speed with which new chemical entities can be synthesized, with applications to human health (e.g., medicines) and sustainability (e.g., catalysts, organic electronics). Educational offerings in the form of open-source code, online courses and trainings, and conference symposia are expected to contribute to the training of trans-disciplinary researchers at the interface of data science and chemistry. Machine learning models for chemistry have started to gain traction for isolated tasks such as computer-aided synthesis planning and reaction condition optimization, but remain limited in terms of the scope of challenges they help address. State-of-the-art statistical models are notoriously data-hungry, do not generalize well, do not build on well-established chemistry theory, and arguably have not yet made genuine discoveries or produced novel insights into new synthetic methods. Dr. Coley will endeavor to address these limitations through four complementary aims associated with the following underlying hypotheses: (1) that reaction condition optimization algorithms can be made more efficient by using literature-trained representations as priors; (2) that models for reaction prediction grounded in physical organic chemistry principles will exhibit greater generalization power than domain-agnostic models; (3) that significant discoveries in organic synthesis can be characterized through novelty detection algorithms and used to bias de novo generation; and (4) that knowledge gaps such as uncertainty about substrate compatibility can be systematically resolved via query-driven active learning and Bayesian optimal experimental design. Each aim will involve a significant technology development effort to improve how machine learning techniques learn and generalize, highlight opportunities for further algorithmic research, and ultimately accelerate the discovery and development of new synthetic organic reactions.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.