title: Estimating pesticide field half-lives from a backpropagation neural network.
abstract: The field half-lives of 110 pesticides were modelled using a backpropagation neural network (NN). The molecules were described by means of the frequency of 17 structural fragments. Before training the NN, different scaling transformations were assayed. Best results were obtained with correspondence factor analysis which also allowed a reduction of dimensionality. The training and testing sets of the NN analysis gave 95.5% and 84.6% of good classifications, respectively. Comparison with discriminant factor analysis showed that a backpropagation NN was more appropriate to model the field half-lives of pesticides.
authors.affiliation: CTIS, Lyon, France.
authors.forename: D
authors.initials: D
authors.lastname: Domine
authors.forename: J
authors.initials: J
authors.lastname: Devillers
authors.forename: M
authors.initials: M
authors.lastname: Chastrette
authors.forename: W
authors.initials: W
authors.lastname: Karcher
mesh_terms: D016002:Discriminant Analysis; D005163:Factor Analysis, Statistical; D006207:Half-Life; D016571:Neural Networks, Computer; D010575:Pesticides / Q000737:chemistry / Q000145:classification / Q000378:metabolism*; D012016:Reference Values; D013329:Structure-Activity Relationship
chemical_list: D010575:Pesticides