Periodic Classification of Local Anaesthetics (Procaine Analogues)

Abstract

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

Fullerite Crystal Thermodynamic Characteristics and the Law of Corresponding States

“…The interassociations in the partial correlation diagram are calculated with our program GraphCor. 60 The partial correlation diagram contains 23 high ( r ≥ 0 75), 12 medium (0 50 ≤ r < 0 75) and 27 low partial correlations (0 25 ≤ r < 0 50). Cluster analysis (CA) 61 was applied to the thermodynamic properties of the inert-gas-C 60 crystals.…”

“…13 14 The C 60 incorporated into artificial lipid membranes was not extracted to toluene, but the extraction became possible once the vesicle was destructed adding KCl. 15 Addition of KCl was required to extract poly(vinylpyrrolidone)-solubilized C [60][61][62][63][64][65][66][67][68][69][70] to toluene. 16 An assembly of randomly packed spheres can represent certain features of the geometry of simple liquids.…”

Information entropy and the classification of local anaesthetics

“…The splits graph is in general agreement with both partial correlation diagrams, dendrograms and binary trees (Figures 3 and 4). The main difference is the partial fusion of C b 2 classes (1,4,6,7,8,14,17,20,21,22,23) and (3,11,12,13,15,16,18,19,24,25,28). However, the results ( Figure 5) should be taken with care, because the former class includes four compounds with the constant <11111> vector (anaesthetics 4, 6, 20 and 23), for which the null standard deviation causes a Pearson correlation coefficient of r = 1 with any local anaesthetic, which is an artifact.…”

“…As entropy is weakly discriminating for classification purposes, the more powerful concept of entropy production and its equipartition conjecture are introduced [13]. In an earlier publication the periodic classification of local anaesthetics (procaine analogues) is analyzed [14]. The aim of the present report is to develop the learning potentialities of the code and, since molecules are more naturally described via a varying size structured representation, the study of general approaches to the processing of structured information.…”

Molecular diversity classification via information theory: A review

“…Valence topological charge-transfer indices for dipoles were obtained [5] and extended to homo/heterocycles and proteins [6]. Information-entropy molecular classification was applied to local anaesthetics [7,8] and inhibitors of human immunodeficiency virus type 1 (HIV-1) [9,10]. It was reported structural classification of complex molecules by artificial intelligence, information entropy and equipartition conjecture, e.g., anti-cancer [11], phenolics [12], flavonoids, analgesics and cardiovascular system drugs [13].…”