The Effect of Variable Selection on the Non-linear Modeling of Oestrogen Receptor Binding

Oestrogen Receptor Binding Affinity (RBA) is often used as a measure of the oestrogenicity

of endocrine disruptingch emicals. Quantitative Structure –Activity Relationship

(QSAR) modellingof the bindingaf finities has been performed by three-dimensional

approaches such as Comparative Molecular Field Analysis (CoMFA). Such techniques are

restricted, however, for chemically diverse sets of chemicals as the alignment of molecules

is complex. The aim of the present study was to use non-linear methods to model the RBA

to the oestrogen receptor of a large diverse set of chemicals. To this end, various variable

selection methods were applied to a large group of descriptors. The methods included

stepwise regression, partial least squares and recursive partitioning (Formal Inference Based

Recursive Modelling, FIRM). The selected descriptors were used in Counter-Propagation

Neural Networks (CPNNs) and Support Vector Machines (SVMs) and the models were

compared in terms of the predictivity of the activities of an external validation set. The

results showed that although there was a certain degree of similarities between the

structural descriptors selected by different methods, the predictive power of the CPNN and

SVM models varied. Although the variables selected by stepwise regression led to poor

CPNN models they resulted in the best SVM model in terms of predictivity. The parameters

selected by some of the FIRM methods were superior in CPNN.