Algorithmic Complexity and Reprogrammability of Chemical Structure Networks

Hector Zenil*, Narsis A. Kiani, Ming Mei Shang, Jesper Tegnér

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks. We profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically, asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes. We arrive at numerical results suggesting a correspondence between some physical, structural and functional properties. Our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties. We conclude that these methods, with their links to chemoinformatics via algorithmic, probability hold promise for future research.

Original languageEnglish (US)
Article number1850005
JournalParallel Processing Letters
Volume28
Issue number1
DOIs
StatePublished - Mar 1 2018

Bibliographical note

Publisher Copyright:
© 2018 The Author(s).

Keywords

  • Kolmogorov-Chaitin complexity
  • Molecular complexity
  • Shannon entropy
  • algorithmic information theory
  • algorithmic probability
  • causal path
  • causality
  • chemical compound complexity
  • information signature

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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