RBFNN

The efficiency of several popular machine-learning methods ANN, SVM, NN, Maximal Margin Linear Programming, RBFNN, and MLR, to build predictive... 

Harding, Popelier, and co-workers [285,286] have employed a variety of quantum chemical approaches in their estimation of the pK s ol oxyacids. In a study of 228 carboxylic acids they used what they call quantum chemical topology to find pK estimates. They tested several different methods, including partial least squares (PLS), support vector machines (SVMs), and radial basis function neural networks (RBFNNs) with Hartree-Fock and density functional calculations, concluding that the SVM models with HF/6-31G calculations were most efficient [285]. Foi a data set of 171 phenols they found that the C-0 bond length provided an effective descriptor for pK estimation [286]. 

Luan et al. (2005PR1454) developed QSPR models to predict the pK, values of a set of 74 neutral and basic drugs via hnear and nonlinear methods. A CODESSA approach was used to derive descriptors and to build linear models RBFNN was used to generate the nonlinear models. Both models used the same descriptors selected by the heuristic method (HM) the descriptors accounted for the relative nitrogen content and polarizabUity of the compounds related to the ease of protonation of the molecules. The results were fair in view of the complexity and relatively large size of the drug molecules (R > 0.6—0.7). 

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