Artificial Intelligence Lab, Department of MIS, University of Arizona / 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105793 |
| Date | 07 1900 |
| Creators | Chow, Hsiao-Hui, Chen, Hsinchun, Ng, Tobun Dorbin, Myrdal, P., Yalkowsky, S.H. |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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