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

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29 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)723-728
Number of pages6
JournalJournal of Chemical Information and Computer Sciences
Volume35
Issue number4
DOIs
StatePublished - Jul 1995

ASJC Scopus subject areas

  • Chemistry(all)
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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