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Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds

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Abstract

An approach based on fragmental descriptors (occurrence number of structural fragments in chemical structures) in conjunction with the artificial neural network technique was developed for predicting the physicochemical properties of organic compounds. The construction of neural network models for predicting the viscosity, density, and saturated vapor pressure for various classes of organic compounds is discussed.

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Authors and Affiliations

  1. Department of Chemistry, M. V. Lomonosov Moscow State University, Leninskie Gory, 119992, Moscow, Russian Federation

    N. V. Artemenko, I. I. Baskin, V. A. Palyulin & N. S. Zefirov

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  1. N. V. Artemenko
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  2. I. I. Baskin
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  3. V. A. Palyulin
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  4. N. S. Zefirov
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Artemenko, N.V., Baskin, I.I., Palyulin, V.A. et al. Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds. Russian Chemical Bulletin 52, 20–29 (2003). https://doi.org/10.1023/A:1022467508832

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