Chemistry in Training and Test Sets Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method

A fragment-based similarity searching method, MOLPRINT 2D, was employed for virtual screening of Escherichia coli dihydrofolate reductase inhibitors. Using the original training set of 50,000 compounds, only marginal enrichment factors (between 1 and 3) could be achieved on the test library. The active structures contained in the training and test libraries represented different types of " chemistry, " that is, different substructural features associated with activity. Training and test sets were pooled in a 2nd step and randomly split into training and test of equal size, with the objective of smoothing out the different chemical characteristics of both libraries. In a 10-fold cross-validation study on the new training and test sets, typically 10-fold enrichment could be found in the first 96 positions, 4-fold enrichment in the first 384 positions, and 3-fold enrichment in the first 1536 positions, corresponding to 6, 10, and 28 hits, respectively (out of a total of 307; activity defined as average residual activity of less than 80%). The conclusions are 2-fold. On one hand, the exact fragment-matching similarity searching method employed here is not capable of finding completely novel hit structures. On the other hand, this study emphasizes the requirement for a comparable distribution of chemical features of the training and test sets. MOLPRINT 2D is freely down-loadable from