title: Quantitative structure-binding relationships (QSBR) and artificial neural networks: improved predictions in drug:cyclodextrin inclusion complexes.
abstract: The application of the principal neural network architecture, namely the multilayer perceptron (MLP), have been developed for obtaining sufficient quantitative structure-binding relationships (QSBR) with high accuracy. To this end a dataset of 17 barbiturates as guests complexing to alpha- and beta-cyclodextrins was examined and the results compared to that of Lopata et al (J. Pharm. Sci., 74, (1995)) who studied the same problem using multiple regression analysis. A series of new and improved algorithms other than the "old fashion" and problematic steepest descent were examined for training the MLP networks. The proposed methods led to substantial gain in both the prediction ability and the computation speed of the resulting models. A specific ANN architecture (4-4-1) trained with the Levenberg-Marquardt algorithm diminished the number of outliers, during its implementation to unseen data (barbiturates), to zero.
authors.affiliation: Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis, Zografou, 157 71 Athens, Greece. loukas@pharm.uoa.gr
authors.forename: Y L
authors.initials: YL
authors.lastname: Loukas
mesh_terms: D000465:Algorithms; D001463:Barbiturates / Q000737:chemistry*; D003505:Cyclodextrins / Q000737:chemistry*; D016208:Databases, Factual; D013329:Structure-Activity Relationship
chemical_list: D001463:Barbiturates; D003505:Cyclodextrins