Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912646672941056 |
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| author | Casetti, Nicholas Anstine, Dylan Isayev, Olexandr Coley, Connor W. |
| author_facet | Casetti, Nicholas Anstine, Dylan Isayev, Olexandr Coley, Connor W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials Casetti, Nicholas Anstine, Dylan Isayev, Olexandr Coley, Connor W. Machine Learning Quantitative Methods 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. |
| title | Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2507.10400 |