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    Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds

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    Author
    Chow, Hsiao-Hui
    Chen, Hsinchun
    Ng, Tobun Dorbin
    Myrdal, P.
    Yalkowsky, S.H.
    Issue Date
    1995-07
    Submitted date
    2004-10-13
    Keywords
    Artificial Intelligence
    Geographic Information Science
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    
    Metadata
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    Citation
    Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds 1995-07, 35(4):723-728 Journal of Chemical Information and Computer Sciences, American Chemical Society
    Journal
    Journal of Chemical Information and Computer Sciences, American Chemical Society
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105793
    Abstract
    This research examined the applicability of using a neural network approach to the estimation of aqueous activity coefficients of aromatic organic compounds from fragmented structural information. A set of 95 compounds was used to train the neural network, and the trained network was tested on a set of 31 compounds. A comparison was made between the results and those obtained using multiple linear regression analysis. With the proper selection of neural network parameters, the backpropagation network provided a more accurate prediction of the aqueous activity coefficients for testing data than did regression analysis. This research indicates that neural networks have the potential to become a useful analytical technique for quantitative prediction of structure-activity relationships.
    Type
    Journal Article (Paginated)
    Language
    en
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