用神经网络加速复杂环化反应路径预测，精准识别关键步骤与立体选择性。
Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
- 结合图枚举与机器学习过滤候选反应路径
- 准确预测活化能并复现天然产物合成中的关键步骤
- 适合药物分子合成中复杂环化反应的机理探索
反应机理搜索工具在揭示反应产物和速率控制步骤方面已展现出潜力。然而，涉及多个协同键变化的反应——这在天然产物合成的关键步骤中常见——会增加搜索难度。为此，我们提出一种针对复杂环化反应的高效机理搜索策略，通过图结构枚举与机器学习中间体筛选相结合，实现低成本路径识别。该方法的核心是采用神经网络势（NNP）AIMNet2-rxn对候选反应路径进行计算评估。本文验证了该模型在估算活化能方面的有效性，成功预测了立体选择性，并重现了天然产物合成中的复杂促成步骤。
原文摘要 · Abstract (English)
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.
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