Algorithmic complexity and reprogrammability of chemical structure networks

by H. Zenil, N.A. Kiani, M.M. Shang, J. Tegnér
Year:2018

Bibliography

Algorithmic complexity and reprogrammability of chemical structure networks
H. Zenil, N.A. Kiani, M.M. Shang, J. Tegnér
Parallel Processing Letters 28 (01), 1850005

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.

DOI: 10.1142/S0129626418500056

Algorithmic complexity and reprogrammability of chemical structure networks.pdf

Keywords

Algorithmic information theory Algorithmic probability Causal path Causality Chemical compound complexity Information signature Kolmogorov-Chaitin complexity Molecular complexity Shannon Entropy
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