J Chem Inf Model - A new approach to radial basis function approximation and its application to QSAR.

Tópicos

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Resumo

We describe a novel approach to RBF approximation, which combines two new elements: (1) linear radial basis functions and (2) weighting the model by each descriptor's contribution. Linear radial basis functions allow one to achieve more accurate predictions for diverse data sets. Taking into account the contribution of each descriptor produces more accurate similarity values used for model development. The method was validated on 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. We also compared the new method with five different QSAR methods implemented in the EPA T.E.S.T. program. Our approach, implemented in the program GUSAR, showed a reasonable accuracy of prediction and high coverage for all external test sets, providing more accurate prediction results than the comparison methods and even the consensus of these methods. Using our new method, we have created models for physicochemical and toxicity endpoints, which we have made freely available in the form of an online service at http://cactus.nci.nih.gov/chemical/apps/cap.

Resumo Limpo

describ novel approach rbf approxim combin two new element linear radial basi function weight model descriptor contribut linear radial basi function allow one achiev accur predict divers data set take account contribut descriptor produc accur similar valu use model develop method valid public data set compris nine physicochem properti five toxic endpoint also compar new method five differ qsar method implement epa test program approach implement program gusar show reason accuraci predict high coverag extern test set provid accur predict result comparison method even consensus method use new method creat model physicochem toxic endpoint made freeli avail form onlin servic httpcactusncinihgovchemicalappscap

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