Robust QSAR models using Bayesian regularized neural networks

F R Burden; D A Winkler

doi:10.1021/jm980697n

Robust QSAR models using Bayesian regularized neural networks

J Med Chem. 1999 Aug 12;42(16):3183-7. doi: 10.1021/jm980697n.

Authors

F R Burden¹, D A Winkler

Affiliation

¹ CSIRO Division of Molecular Science, Private Bag 10, Clayton South MDC, Clayton, Victoria 3169, Australia.

PMID: 10447964
DOI: 10.1021/jm980697n

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.

Publication types

Comparative Study

MeSH terms

Drug Design*
Neural Networks, Computer*
Regression Analysis
Structure-Activity Relationship*