Robust QSAR Models Using Bayesian Regularized Neural Networks
Journal of Medicinal Chemistry1999Vol. 42(16), pp. 3183–3187
Citations Over TimeTop 10% of 1999 papers
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.
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