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

Persistent Link:
http://hdl.handle.net/10150/105793
Title:
Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds
Author:
Chow, Hsiao-Hui; Chen, Hsinchun; Ng, Tobun Dorbin; Myrdal, P.; Yalkowsky, S.H.
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
Issue Date:
Jul-1995
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105793
Submitted date:
2004-10-13
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
Keywords:
Artificial Intelligence; Geographic Information Science
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab

Full metadata record

DC FieldValue Language
dc.contributor.authorChow, Hsiao-Huien_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorNg, Tobun Dorbinen_US
dc.contributor.authorMyrdal, P.en_US
dc.contributor.authorYalkowsky, S.H.en_US
dc.date.accessioned2004-10-13T00:00:01Z-
dc.date.available2010-06-18T23:34:33Z-
dc.date.issued1995-07en_US
dc.date.submitted2004-10-13en_US
dc.identifier.citationUsing 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 Societyen_US
dc.identifier.urihttp://hdl.handle.net/10150/105793-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThis 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectGeographic Information Scienceen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleUsing Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compoundsen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of Chemical Information and Computer Sciences, American Chemical Societyen_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.