Advances in the Inference of Chemical Reaction Networks from Time Series Data: A Systematic Survey

Kreikemeyer, Justin N. and Uhrmacher, Adelinde M. (2026) Advances in the Inference of Chemical Reaction Networks from Time Series Data: A Systematic Survey. arXiv, (Submitted)

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Abstract

We provide an accessible introduction and overview of the machine-learning and system-identification problem of inferring chemical reaction networks (CRNs) from time series data. Specifically, given data on the temporal evolution of abundances or concentrations of entities, the task is to infer a set of reactions that could have produced this data, including their kinetic laws and corresponding parameters. Solving this problem is expected to significantly accelerate the prediction and understanding of population dynamics by automating the time-intensive modeling process. In a systematic survey of peer-reviewed and preprint articles published until December 2025, we identified 71 publications detailing 68 distinct methods motivated by chemical and biological applications. Based on this large sample of the literature, we provide a current perspective on the methodological developments, propose a three-dimensional taxonomy, and highlight promising future directions. Motivated by the absence of a recent state-of-the-art synthesis and scattered progress across the application domains of CRNs, we view this work as an important step toward further methodological progress.