title: Application of neural networks in the QSAR analysis of percent effect biological data: comparison with adaptive least squares and nonlinear regression analysis.
abstract: Artificial neural networks (ANN) can be used for the direct QSAR analysis of percent effect biological data, thus avoiding the bias introduced by arbitrarily chosen classes and the loss of information due to prior classification. For two data sets the ANN results are compared with those obtained by adaptive least squares and nonlinear regression analyses. In comparison with the other methods the neural network shows higher predictive power and does not require an explicit equation relating the observed effect to physicochemical descriptors.
authors.affiliation: Forschungsinstitut Borstel, Med.-Pharm. Chemie, Borstel, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Wiese
authors.forename: K J
authors.initials: KJ
authors.lastname: Schaper
mesh_terms: D000818:Animals; D004305:Dose-Response Relationship, Drug; D007306:Insecticides / Q000737:chemistry* / Q000633:toxicity; D016018:Least-Squares Analysis; D008925:Mites; D016571:Neural Networks, Computer; D011084:Polycyclic Aromatic Hydrocarbons / Q000737:chemistry* / Q000633:toxicity; D012044:Regression Analysis; D013329:Structure-Activity Relationship; D013449:Sulfonamides / Q000737:chemistry* / Q000633:toxicity
chemical_list: D007306:Insecticides; D011084:Polycyclic Aromatic Hydrocarbons; D013449:Sulfonamides