Random forest models

Random Forest model with key properties) (Random Forest model with key properties)... 

Figure 1 Random Forest models of dogP and TPSA against relative risk.

It is interesting to note that various QSAR/QSPR models from an array of methods can be very different in both complexity and predictivity. For example, a simple QSPR equation with three parameters can predict logP within one unit of measured values (43) while a complex hybrid mixture discriminant analysis-random forest model with 31 computed descriptors can only predict the volume of distribution of drugs in humans within about twofolds of experimental values (44). The volume of distribution is a more complex property than partition coefficient. The former is a physiological property and has a much higher uncertainty in its experimental measurements while logP is a much simpler physicochemical property and can be measured more accurately. These and other factors can dictate whether a good predictive model can be built. 

As an aside to those following along with the code in the listings, please note that your results will not exactly match those listed here. There is a stochastic component to the selection of training and test sets, as well as the construction of the random forest models. While your results probably won t match exactly what is reported here, they should be similar. 

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