Inferring molecular complexity from mass spectrometry data using machine learning

Timothy D. Gebhard*, Aaron C. Bell*, Jian Gong*, Jaden J. A. Hastings*, G. Matthew Fricke, Nathalie Cabrol, Scott Sandford, Michael Phillips, Kimberley Warren-Rhodes, Atılım Güneş Baydin

Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022, December 2022

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Cite this paper

@article{Gebhard_2022,
  title         = {{Inferring molecular complexity from mass spectrometry data using machine learning}},
  author        = {Timothy D. Gebhard, Aaron C. Bell, Jian Gong, Jaden J. A. Hastings, G. Matthew Fricke, Nathalie Cabrol, Scott Sandford, Michael Phillips, Kimberley Warren-Rhodes, Atılım Güneş Baydin},
  year          = 2022,
  month         = 12,
  addendum      = {Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2022},
}

Poster

Abstract:

Molecular complexity has been proposed as a potential agnostic biosignature — in other words: a way to search for signs of life beyond Earth without relying on “life as we know it.” More than one way to compute molecular complexity has been proposed, so comparing their performance in evaluating experimental data collected in situ, such as on board a probe or rover exploring another planet, is imperative. Here, we report the results of an attempt to deploy multiple machine learning (ML) techniques to predict molecular complexity scores directly from mass spectrometry data. Our initial results are encouraging and may provide fruitful guidance toward determining which complexity measures are best suited for use with experimental data. Beyond the search for signs of life, this approach is likewise valuable for studying the chemical composition of samples to assist decisions made by the rover or probe, and may thus contribute toward supporting the need for greater autonomy.