In silico Support for Eschenmoser’s Glyoxylate Scenario

Israel Journal of Chemistry, Wiley, ISSN 1869-5868

Volume 55, 8, 2015

DOI:10.1002/ijch.201400187, Dimensions: pub.1030526937,


Stadler, Peter F. (2) (3) (4) (5) (6) (7)



  1. (1) Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, DK‐5230 Odense M (Denmark)
  2. (2) University of Vienna, grid.10420.37
  3. (3) Fraunhofer Institute for Cell Therapy and Immunology, Leipzig (Germany)
  4. (4) Leipzig University, grid.9647.c
  5. (5) Max Planck Institute for Mathematics in the Sciences, Leipzig (Germany)
  6. (6) Santa Fe Institute, Santa Fe (USA)
  7. (7) University of Copenhagen, grid.5254.6, KU








A core topic of research in prebiotic chemistry is the search for plausible synthetic routes that connect the building blocks of modern life, such as sugars, nucleotides, amino acids, and lipids to “molecular food sources” that were likely to have been abundant on early Earth. In a recent contribution, Albert Eschenmoser emphasised the importance of catalytic and autocatalytic cycles in establishing such abiotic synthesis pathways. The accumulation of intermediate products furthermore provides additional catalysts that allow pathways to change over time. We show here that generative models of chemical spaces based on graph grammars make it possible to study such phenomena in a systematic manner. In addition to reproducing the key steps of Eschenmoser’s hypothesis paper, we discovered previously unexplored potentially autocatalytic pathways from HCN to glyoxylate. A cascade of autocatalytic cycles could efficiently re-route matter, distributed over the combinatorial complex network of HCN hydrolysation chemistry, towards a potential primordial metabolism. The generative approach also has it intrinsic limitations: the unsupervised expansion of the chemical space remains infeasible due to the exponential growth of possible molecules and reactions between them. Here, in particular, the combinatorial complexity of the HCN polymerisation and hydrolysation networks forms the computational bottleneck. As a consequence, guidance of the computational exploration by chemical experience is indispensable.


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