Despite remarkable advances in computational chemistry, prediction of reaction mechanisms is still challenging, because investigating all possible reaction pathways is computationally prohibitive due to the high complexity of chemical space. A feasible strategy for efficient prediction is to utilize chemical heuristics. Here, we propose a novel approach to rapidly search reaction paths in a fully automated fashion by combining chemical theory and heuristics. A key idea of our method is to extract a minimal reaction network composed of only favorable reaction pathways from the complex chemical space through molecular graph and reaction network analysis. This can be done very efficiently by exploring the routes connecting reactants and products with minimum dissociation and formation of bonds. Finally, the resulting minimal network is subjected to quantum chemical calculations to determine kinetically the most favorable reaction path at the predictable accuracy. As example studies, our method was able to successfully find the accepted mechanisms of Claisen ester condensation and cobalt-catalyzed hydroformylation reactions.

中文翻译：

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通过分子图和反应网络分析有效预测反应路径†

尽管计算化学取得了显着进展，但反应机制的预测仍然具有挑战性，因为由于化学空间的高度复杂性，研究所有可能的反应路径在计算上是令人望而却步的。有效预测的一个可行策略是利用化学启发法。在这里，我们提出了一种结合化学理论和启发式方法以全自动方式快速搜索反应路径的新方法。我们方法的一个关键思想是通过分子图和反应网络分析从复杂的化学空间中提取仅由有利的反应路径组成的最小反应网络。通过探索以最少的解离和形成键连接反应物和产物的路线，可以非常有效地完成这一任务。最后，对所得的最小网络进行量子化学计算，以便以可预测的精度在动力学上确定最有利的反应路径。作为示例研究，我们的方法能够成功找到克莱森酯缩合和钴催化加氢甲酰化反应的公认机制。