title: Robust QSAR models using Bayesian regularized neural networks.
abstract: We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors is illustrated.
authors.affiliation: CSIRO Division of Molecular Science, Private Bag 10, Clayton South MDC, Clayton, Victoria 3169, Australia.
authors.forename: F R
authors.initials: FR
authors.lastname: Burden
authors.forename: D A
authors.initials: DA
authors.lastname: Winkler
mesh_terms: D015195:Drug Design; D016571:Neural Networks, Computer; D012044:Regression Analysis; D013329:Structure-Activity Relationship