Studies on the intercalation of anticancer compounds with DNA can provide useful suggestions and guidance for the design of new and more efficient anticancer drugs.A quant. structure-property relationship (QSPR) study of a series of anticancer and candidate anticancer drugs with calf thymus DNA (ct-DNA) was performed.Constitutional, Topol., and WHIM descriptors, as well as GETAWAY, 3D-MoRSE, and Aromaticity Indexes descriptors generated from Dragon, were selected to describe the mols.The resampling by half-means method was used to detect the outlier mols.Self-organizing map was used to split the original dataset into training and test set.Genetic algorithm-multiple linear regression technique was used to establish QSPR model for training set.Finally, the best four-mol. descriptor model was developed on a training set of mols. and the external validation was performed on test set of mols.The stability and predictability of QSPR model were determined with the leave-one-out cross-validated variance and the external-validated variance.This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-DNA intercalations, and be useful in predicting the binding affinity of other compounds with DNA.