Algorithmic Complexity and Reprogrammability of Chemical Structure Networks
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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AbstractHere 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.
CitationZenil H, Kiani NA, Shang M, Tegnér J (2018) Algorithmic Complexity and Reprogrammability of Chemical Structure Networks. Parallel Processing Letters 28: 1850005. Available: http://dx.doi.org/10.1142/S0129626418500056.
SponsorsH.Z. is thankful for the support of the Swedish Research Council (Vetenskapsradet) Grant No. 2015-05299.
PublisherWorld Scientific Pub Co Pte Lt
JournalParallel Processing Letters
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