Antitumor agents.: 213.: Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighbor QSAR method

We have applied a variable selection k nearest neighbor quantitative structure-activity relationship (kNN QSAR) method to develop predictive QSAR models for 157 epipodophyllotoxins synthesized previously in our ongoing effort to develop potential anticancer agents. QSAR models were generated using multiple topological descriptors of chemical structures, including molecular connectivity indices (MCI) and molecular operating environment descriptors. The 157 compounds were separated into several training and test sets. The robustness of QSAR models was characterized by the values of the internal leave one out cross-validated R-2 (q(2)) for the training set and external predictive R-2 for the test set. The significance of the training set models was confirmed by statistically higher values of q(2) for the original data set as compared to q(2) values for the same data set with randomly shuffled activities. kNN QSAR models were compared with those obtained with the comparative molecular field analysis method; the kNN QSAR approach afforded models with higher values of both q(2) and predictive R-2. One of the best models obtained from kNN analysis using MCI as descriptors provided q(2) and predictive R-2 values of 0.60 and 0.62, respectively. QSAR models developed in these studies shall aid in future design of novel potent epipodophyllotoxin derivatives.