Computationally Driven Synthesis of Paxilline-Type Indole Diterpenoids

About This Grant

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry Professor Timothy Newhouse of Yale University is studying the development of synthetic routes to diterpenoids. The synthesis of small molecules is one of the rate-limiting steps across disciplines from materials science to medicinal chemistry. To overcome this bottleneck in the discovery process, we need methodological advancements and improvements to the synthesis design process. The long-term goal of this proposal is to apply computational strategies to synthetic planning to access structurally complex natural products, and in this proposal these efforts are focused on synthesis of diterpenoids. The approach to model development described in this proposal can be applied to any synthetic transformation and would be enabling and thus broadly impactful whenever that transformation’s short-term experimental evaluation is not possible. Moreover, strategic partnerships within and around the Yale community will bring science to K-12 audiences. The design of a synthetic pathway to a desired molecule is generally conducted by human analysis although computational approaches are beginning to emerge. This proposal outlines the development of several artificial intelligence-based tools to predict the yield of common carbon-carbon bond forming cyclization reactions. Additionally, generative modelling is proposed to design ligands, substrates, and routes to natural products. These computational methods will enable the planning and synthesis of natural products and analogs. High-risk yet high-reward plans are de-risked through the use of machine learning models, thereby allowing efficient and expedient laboratory access to synthetically challenging molecular scaffolds. 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.