TY - JOUR
T1 - Algorithmic Complexity and Reprogrammability of Chemical Structure Networks
AU - Zenil, Hector
AU - Kiani, Narsis A.
AU - Shang, Ming-mei
AU - Tegner, Jesper
N1 - KAUST Repository Item: Exported on 2021-02-23
Acknowledgements: H.Z. is thankful for the support of the Swedish Research Council (Vetenskapsradet) Grant No. 2015-05299.
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/627192
UR - http://arxiv.org/abs/1802.05856v1
UR - http://www.scopus.com/inward/record.url?scp=85044738221&partnerID=8YFLogxK
U2 - 10.1142/S0129626418500056
DO - 10.1142/S0129626418500056
M3 - Article
AN - SCOPUS:85044738221
SN - 0129-6264
VL - 28
SP - 1850005
JO - Parallel Processing Letters
JF - Parallel Processing Letters
IS - 01
ER -