Periodic Classification of Local Anaesthetics (Procaine Analogues)

International Journal of Molecular Sciences

Francisco Torrens¹ 

[1] ¹Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P. O. Box 22085, E-46071 València, Spain ²Departamento de Ciencias Experimentales, Facultad de Ciencias Experimentales, Universidad Católica de Valencia San Vicente Mártir, Guillem de Castro-106, E-46003 València, Spain

关键词: periodic property;    periodic table;    periodic law;    classification;    information entropy;    equipartition conjecture;    principal component analysis;    cluster analysis;    local anaesthetic;    procaine analogue.;   

DOI  :  10.3390/i8010012

来源: mdpi

【 摘 要 】

Algorithms for classification are proposed based on criteria (information entropyand its production). The feasibility of replacing a given anaesthetic by similar ones in thecomposition of a complex drug is studied. Some local anaesthetics currently in use areclassified using characteristic chemical properties of different portions of their molecules.Many classification algorithms are based on information entropy. When applying theseprocedures to sets of moderate size, an excessive number of results appear compatible withdata, and this number suffers a combinatorial explosion. However, after the equipartitionconjecture, one has a selection criterion between different variants resulting fromclassification between hierarchical trees. According to this conjecture, for a given charge orduty, the best configuration of a flowsheet is the one in which the entropy production is mostuniformly distributed. Information entropy and principal component analyses agree. Theperiodic law of anaesthetics has not the rank of the laws of physics: (1) the properties ofanaesthetics are not repeated; (2) the order relationships are repeated with exceptions. Theproposed statement is: The relationships that any anaesthetic p has with its neighbour p 1are approximately repeated for each period.

【 授权许可】

CC BY   
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.