Abstract
Bayesian regularized artificial neural networks (BRANNs) are used in the development of quantitative SAR models. These networks have the potential to solve several problems that 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 a wide range of problems, including target-based QSAR, ADMET modeling and eukaryotic promoter finding, is illustrated.
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