This paper reports on the results of experiments regard- ing a biomedical data set describing blood-brain barrier penetration ability of molecules. In this data set 415 cases represent organic compounds with known steady-state con- centrations of a drug in the brain and blood. In our ex- periments we used two different discretization algorithms, based on agglomerative and divisive approaches of cluster analysis, respectively, and two different approaches to miss- ing attribute values: deletion of cases with missing attribute values and deletion of attributes with missing values. Using ten-fold cross validation we concluded that the best strat- egy is based on a divisive approach of cluster analysis and deleting cases affected by missing attribute values. More- over, prediction accuracy of this strategy is comparable with the other successful approaches reported in this area.