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In their recent paper [1] Good and Hermsmeier discuss the effects of test set selection on the evaluation and comparison of SAR methodologies. In particular they examine the impact that analogue effect has on overestimating the predictivness of drug vs non-drug models built by Bayeisan modeling. The analogue effect is a result of most drug and druglike compendia having extensive sets of analogues. When selecting random test sets this tends to ensure that test and training sets both contain members of the same series, which in turn means that the predictivity of a model is greater than it might otherwise be.

Is learning drugs the same as learning non-drugs?