title: A neural network based prediction of octanol-water partition coefficients using atomic5 fragmental descriptors.
abstract: An artificial neural network based approach using Atomic5 fragmental descriptors has been developed to predict the octanol-water partition coefficient (logP). We used a pre-selected set of organic molecules from PHYSPROP database as training and test sets for a feedforward neural network. Results demonstrate the superiority of our non-linear model over the traditional linear method.
authors.affiliation: Department of Chemical Information Technology, Budapest University of Technology and Economics, Szent Gellért tér 4., H-1111 Budapest, Hungary.
authors.forename: László
authors.initials: L
authors.lastname: Molnár
authors.forename: György M
authors.initials: GM
authors.lastname: Keseru
authors.forename: Akos
authors.initials: A
authors.lastname: Papp
authors.forename: Zsolt
authors.initials: Z
authors.lastname: Gulyás
authors.forename: Ferenc
authors.initials: F
authors.lastname: Darvas
mesh_terms: D008956:Models, Chemical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D000442:Octanols / Q000032:analysis / Q000737:chemistry*; D012995:Solubility; D014867:Water / Q000032:analysis / Q000737:chemistry*
chemical_list: D000442:Octanols; D014867:Water