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Doklady Chemistry, Vol. 383, Nos. 4–6, 2002, pp. 114–116. Translated from Doklady Akademii Nauk, Vol. 383, No. 6, 2002, pp. 771–773. Original Russian Text Copyright © 2002 by Artemenko, Palyulin, Zefirov. CHEMISTRY Neural-Network Model of the Lipophilicity of Organic Compounds Based on Fragment Descriptors N. V. Artemenko, V. A. Palyulin, and Academician N. S. Zefirov Received December 28, 2001 In recent years, interest has been significantly condensed systems; their halogen, nitro, nitroso, cyano, increased in artificial neural networks of different types and sulfo derivatives; cycloalkanes and cycloalkenes; and architectures as a powerful tool for modeling com- adamantanes; heterocyclic and polycyclic structures; plex nonlinear relationships. Previously, we success- sulfones; sulfoxides; amino acids and amides; hydra- fully modeled some physicochemical properties [1] on zines; hydrazides; semicarbazides; amines; imines; the basis of only fragment descriptors; results of this quaternary ammonium salts; carboxylic, phosphoric, modeling demonstrated that the use of the proposed set and sulfonic acids; carbamates; crown ethers; sugars; of fragment descriptors and their nonlinear modifica- steroids; prostaglandins; alkaloids; antibiotics; and tions show considerable promise. In this work, we con- compounds of boron, mercury, germanium, selenium, sider neural-network modeling of the lipophilicity of lead, gold, etc. In the compilation of the database,
Doklady Chemistry – Springer Journals
Published: Oct 10, 2004
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