Modular Synthesis of Saturated, Nitrogen-Containing Heterocycles Guided by Mechanism-Aware Machine Learning Models

About This Grant

1 PROJECT SUMMARY 2 Successful development of new active pharmaceutical ingredients (APIs) requires synthesizing many 3 structurally related compounds to optimize pharmacokinetic properties related to absorption, distribution, 4 metabolism, excretion, and toxicology. However, the expense of time and resources needed to synthesize each 5 candidate makes API optimization a bottleneck. As a result, researchers have developed a relatively short list of 6 expedient synthetic methods on which they rely to develop APIs. These methods tend to involve fragment 7 couplings that forge new C-C, C-N, or C-O bonds by joining two building blocks, which allows those seeking new 8 APIs to purchase libraries of suitable building blocks and explore their pairwise couplings in modular fashion. 9 The modularity of this approach means that each new fragment increases the quantity of structures that can be 10 explored in a nonlinear fashion. However, some of the most frequent substructures in APIs—saturated medium- 11 sized nitrogen-containing heterocycles—are not well represented in commercial catalogues of building blocks, 12 due to limitations in state-of-the-art synthetic methods used to produce them. The ability to create custom building 13 blocks in a modular fashion, which would enable both more thorough and more efficient structure optimizations 14 of APIs, would represent a significant advance in modern synthetic chemistry. However, current synthetic 15 limitations make exploration of 3D structural variants of these heterocycles difficult. 16 This proposal outlines a strategy to develop new catalytic methods that will convert readily accessible starting 17 materials into structurally complex heterocyclic building blocks in modular fashion. The modularity of the 18 proposed methods, the accessibility of starting materials, and the proposed catalyst control of stereochemistry 19 in these chiral products will make exploration of new, chiral variants of these important building blocks more 20 practical. The proposed research includes a tandem fragment coupling-cyclization approach to assemble 21 multiple building blocks to construct structurally complex, medium-sized, saturated heterocycles. Mechanistic 22 experiments and computational exploration of key mechanistic steps is proposed so that both activity and 23 selectivity of the catalysts can be understood and iteratively improved with the assistance of new data 24 representations and machine learning. Accomplishing these goals would provide practitioners access to diverse 25 structural variants of important building blocks for API development, as well as demonstrate the future role of 26 machine learning as a tool that can accelerate the development of methods with a large scope.