title: Using backpropagation networks for the estimation of aqueous activity coefficients of aromatic organic compounds.
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.
authors.affiliation: Department of Pharmacy Practice, University of Arizona, Tucson 85721, USA.
authors.forename: H
authors.initials: H
authors.lastname: Chow
authors.forename: H
authors.initials: H
authors.lastname: Chen
authors.forename: T
authors.initials: T
authors.lastname: Ng
authors.forename: P
authors.initials: P
authors.lastname: Myrdal
authors.forename: S H
authors.initials: SH
authors.lastname: Yalkowsky
mesh_terms: D006838:Hydrocarbons / Q000737:chemistry* / Q000494:pharmacology; D015394:Molecular Structure; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012996:Solutions; D013329:Structure-Activity Relationship; D014867:Water
chemical_list: D006838:Hydrocarbons; D012996:Solutions; D014867:Water