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Classification of Complex Molecules

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Foundations of Computational Intelligence Volume 5

Part of the book series: Studies in Computational Intelligence ((SCI,volume 205))

Abstract

Algorithms for classification and taxonomy based on criteria, e.g., information entropy and its production are proposed. In molecular classification, the feasibility of replacing a given molecule (e.g, 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. In taxonomy, the detailed comparison of the sequences (primary structures) of biomolecules, proteins or nucleic acids, allows the reconstruction of a molecular phylogenetic tree for some species, e.g. the 1918 influenza virus. The method is applied to the classifications of: (1) indazolols (action against Trichomonas vaginalis), (2) fullerenes, fullerite and single-wall carbon nanotubes, (3) living and heat-inactivated lactic acid bacteria against cytokines, (4) phylogenesis of avian birds and of the 1918 influenza virus, (5) local anaesthetics (analogues of procaine), (6) transdermal-delivery percutaneous enhancers, (7) quantitative structure-activity relationship modelling of anti-human immunodeficiency virus type 1 (HIV-1) compounds and (8) HIV-1 inhibitors. If, in the calculation of entropy associated with the phylogenetic tree, a species is systematically omitted, the difference between the entropy with and without this species can be considered as a measure of the species entropy. Such contributions may be studied with the equipartition conjecture. Obviously, it is not within the scope of our simulation method to replace biological tests of drugs or field data in palaeontology, but such simulation methods can be useful to assert priorities in detailed experimental research. Available experimental and field data should be examined by different classification algorithms to reveal possible features of real biological significance.

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Authors and Affiliations

  1. Institut Universitari de Ciència Molecular, Universitat de València, Edifici d’Instituts de Paterna, P.O. Box 22085, E 46071, València, Spain

    Francisco Torrens

  2. Instituto Universitario de Medio Ambiente y Ciencias Marinas, Universidad Católica de Valencia San Vicente Mártir, Guillem de Castro 94, E 46003, València, Spain

    Gloria Castellano

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  1. Francisco Torrens
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  2. Gloria Castellano
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  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Washington, USA

    Ajith Abraham

  2. Faculty of Computers and Information, Information Technology Department, Cairo University, 5 Ahmed Zewal St., Orman, Giza

    Aboul-Ella Hassanien

  3. Dept. Computer Science, Technical University Ostrava, Tr. 17. Listopadu 15, 708 33, Ostrava, Czech Republic

    Václav Snášel

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Torrens, F., Castellano, G. (2009). Classification of Complex Molecules. In: Abraham, A., Hassanien, AE., Snášel, V. (eds) Foundations of Computational Intelligence Volume 5. Studies in Computational Intelligence, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01536-6_11

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