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Neural-Network Model of the Lipophilicity of Organic Compounds Based on Fragment Descriptors

Neural-Network Model of the Lipophilicity of Organic Compounds Based on Fragment Descriptors 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, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Doklady Chemistry Springer Journals

Neural-Network Model of the Lipophilicity of Organic Compounds Based on Fragment Descriptors

Doklady Chemistry , Volume 383 (6) – Oct 10, 2004

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References (6)

Publisher
Springer Journals
Copyright
Copyright © 2002 by MAIK “Nauka/Interperiodica”
Subject
Chemistry; Chemistry/Food Science, general; Industrial Chemistry/Chemical Engineering
ISSN
0012-5008
eISSN
1608-3113
DOI
10.1023/A:1015408423459
Publisher site
See Article on Publisher Site

Abstract

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,

Journal

Doklady ChemistrySpringer Journals

Published: Oct 10, 2004

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