We compare a number of data mining and statistical methods on the drug design problem of modeling molecular structure-activity relationships. The relationships can be use to identify active compounds base on their chemical structures from a large inventory of chemical compounds. The data set of this application has a highly skewed class distribution, in which only 2%of the compounds are considered active. We apply a number of classification methods to this extremely imbalance data set and propose to use different performance measures to evaluate these methods. We report our findings on the characteristics of the performance measures, the effect of using pruning techniques in this application and a comparison of local learning methods with global techniques. We also investigate whether reducing the imbalance in the training data by up-sampling or down-sampling would improve the predictive performance.