Tutorial Classifying Compounds into Different Modes of Action

Tutorial Classifying Compounds into Different Modes of Action 

The previously mentioned data set with a total of 115 compounds has already been studied by other statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis, and the Partial Least Squares (PLS) method [39]. Thus, the choice and selection of descriptors has already been accomplished. 

The objective of this study is to show how data sets of compounds for which dif-ferent biological activities have been determined can be studied. It will be shown how the use of a counter-propagation neural networb can lead to new insights [46]. The cmpha.si.s in this example is placed on the comparison of different network architectures and not on quantitative results. 

The same structure representation as the one taken in the original study [39] is selected in order to show some possibilities evolving from working with a neural network method. Tabic 10.1-1 gives the ten descriptors chosen lor the representation of the 115 molecules of the data set. 

The data set was then sent into a counter-propagation (CPG) network consisting of 13 X 9 neurons with 10 layers (one for each descriptor) in the input block and one layer in the output block (figure 10.1-9), with the output values having nine different values corresponding to the nine different MOA. 

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