Anticipating the Selectivity of Cyclization Reaction Pathways with Neural Network Potentials

2025-07-14

AI Summary

Predicting stereoselectivity in complex cyclization reactions—particularly those involving multibond concerted transformations—remains challenging due to high computational cost and limited accuracy of conventional methods. To address this, we propose a three-tier mechanistic search strategy: graph-based enumeration of reaction pathways, machine-learning–guided stereochemical filtering, and neural-network–based potential energy evaluation. We innovatively employ AIMNet2-rxn as a fast, quantum-mechanically accurate surrogate potential for reaction intermediates, enabling millisecond-scale computation of energies and gradients. Integrated with reaction graph generation and stereochemistry-aware screening, the method efficiently identifies dominant cyclization pathways. Validated on key natural product cyclizations—including Diels–Alder ([4+2]) and Nazarov reactions—it reproduces experimental stereoselectivity with >92% prediction accuracy, while reducing computational cost by 2–3 orders of magnitude relative to DFT. This work establishes a scalable, AI-driven paradigm for rational design of complex concerted cyclizations.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Search and Optimization: Metareasoning and Metaheuristics
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Predicting cyclization reaction selectivity using neural networks
• Simplifying mechanism search for complex concerted bond changes
• Cost-effective identification of key reaction steps with AI

Innovation

• Combining graph-based enumeration with machine learning
• Using neural network potential AIMNet2-rxn
• Estimating activation energies and stereoselectivity