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dc.contributor.authorZenil, Hector
dc.contributor.authorKiani, Narsis A.
dc.contributor.authorShang, Ming-mei
dc.contributor.authorTegner, Jesper
dc.date.accessioned2018-04-25T12:50:17Z
dc.date.available2018-02-27T09:00:10Z
dc.date.available2018-04-25T12:50:17Z
dc.date.issued2018-04
dc.identifier.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.
dc.identifier.issn0129-6264
dc.identifier.issn1793-642X
dc.identifier.doi10.1142/S0129626418500056
dc.identifier.urihttp://hdl.handle.net/10754/627192
dc.description.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.
dc.description.sponsorshipH.Z. is thankful for the support of the Swedish Research Council (Vetenskapsradet) Grant No. 2015-05299.
dc.language.isoen
dc.publisherWorld Scientific Pub Co Pte Lt
dc.relation.urlhttp://arxiv.org/abs/1802.05856v1
dc.relation.urlhttp://arxiv.org/pdf/1802.05856v1
dc.relation.urlhttps://www.worldscientific.com/doi/abs/10.1142/S0129626418500056
dc.rightsThis is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectalgorithmic information theory
dc.subjectalgorithmic probability
dc.subjectcausal path
dc.subjectcausality
dc.subjectchemical compound complexity
dc.subjectinformation signature
dc.subjectKolmogorov-Chaitin complexity
dc.subjectMolecular complexity
dc.subjectShannon entropy
dc.titleAlgorithmic Complexity and Reprogrammability of Chemical Structure Networks
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalParallel Processing Letters
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionAlgorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris, , France
dc.contributor.institutionScience for Life Laboratory, SciLifeLab, Stockholm, , Sweden
dc.contributor.institutionUnit of Computational Medicine, Department of Medicine, Karolinska Institute, Stockholm, , Sweden
dc.contributor.institutionAlgorithmic Dynamics Lab, Centre for Molecular Medicine, Karolinska Institute, Stockholm, , Sweden
dc.identifier.arxividarXiv:1802.05856
kaust.personTegner, Jesper
refterms.dateFOA2018-06-14T06:10:28Z
dc.date.published-online2018-04
dc.date.published-print2018-03
dc.date.posted2018-02-16


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This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.
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