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Fully Automated Generation of Prebiotically Relevant Reaction Networks from Optimized Nanoreactor Simulations

A. Stan 2022 J. Chem. Theory Comput.


Alexandra Stan, Beatriz von der Esch, and Christian Ochsenfeld

J. Chem. Theory Comput.


The nanoreactor approach first introduced by the group of Martı́nez [Wang et al. Nat. Chem.2014,6, 1044–1048] has recently attracted much attention because of its ability to accelerate the discovery of reaction pathways. Here, we provide a comprehensive study of various simulation parameters and present an alternative implementation for the reactivity-enhancing spherical constraint function, as well as for the detection of reaction events. In this context, a fully automated postsimulation evaluation procedure based on RDKit and NetworkX analysis is introduced. The chemical and physical robustness of the procedure is examined by investigating the reactivity of selected homogeneous systems. The optimized procedure is applied at the GFN2-xTB level of theory to a system composed of HCN molecules and argon atoms, acting as a buffer, yielding prebiotically plausible primary and secondary precursors for the synthesis of RNA. Furthermore, the formose reaction network is explored leading to numerous sugar precursors. The discovered compounds reflect experimental findings; however, new synthetic routes and a large collection of exotic, highly reactive molecules are observed, highlighting the predictive power of the nanoreactor approach for unraveling the reactive manifold.