Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials

TL;DR

This paper introduces a neural network potential-based method to efficiently predict and analyze complex intramolecular cyclization reactions, aiding natural product synthesis by accurately estimating activation energies and stereoselectivity.

Contribution

The study presents a novel approach combining graph-based enumeration and neural network potentials to streamline reaction mechanism searches for complex cyclizations.

Findings

Neural network potential accurately estimates activation energies.

Method correctly predicts stereoselectivity in reactions.

Recapitulates key steps in natural product synthesis.

Abstract

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.