ArticleChemical science2022
An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Autocatalytic cores in the diluted regime: classification and properties.Journal of mathematical biology · 2026Article
- Thermodynamic consistency of autocatalytic cycles.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Assessment of Stoichiometric Autocatalysis across Element Groups.Journal of the American Chemical Society · 2023Article
- A robust, agnostic molecular biosignature based on machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Carbonyl Migration in Uronates Affords a Potential Prebiotic Pathway for Pentose Production.JACS Au · 2023Article
- The Effects of Iron on In Silico Simulated Abiotic Reaction Networks.Molecules (Basel, Switzerland) · 2022Article
- Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab.Accounts of chemical research · 2022Article
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Authors and funding
10 authors.
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Abstract
A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of molecular complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chemistry that builds molecular complexity. We present here an open-source chemoinformatic workflow to model abiological chemistry to discover such entailment. This pipeline automates generation of chemical reaction networks and their analysis to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system by vetting it against experimental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows.
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Registered trials
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