Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
This work addresses a domain-specific problem for chemists by providing a cost-effective tool to expedite exploration of complex reaction mechanisms, though it is incremental as it builds on existing methods.
The paper tackled the challenge of predicting reaction pathways for complex cyclization reactions in natural product synthesis by developing a mechanism search strategy that combines graph-based enumeration with neural network potentials, resulting in correct anticipation of stereoselectivity and recapitulation of enabling steps.
Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving several concerted bond changes - as can be found in many key steps of natural product synthesis - can complicate the search process. To mitigate these complications, we present a mechanism search strategy particularly suited to help expedite exploration of an exemplary family of such complex reactions, cyclizations. We provide a cost-effective strategy for identifying relevant elementary reaction steps by combining graph-based enumeration schemes and machine learning techniques for intermediate filtering. Key to this approach is our use of a neural network potential (NNP), AIMNet2-rxn, for computational evaluation of each candidate reaction pathway. In this article, we evaluate the NNP's ability to estimate activation energies, demonstrate the correct anticipation of stereoselectivity, and recapitulate complex enabling steps in natural product synthesis.