Quantitative structure-property relationship study on the intercalation of anticancer drugs with ct-DNA

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 quantitative structure-property relationship (QSPR) study of a series of anticancer and candidate anticancer drugs with calf thymus DNA (ct-DNA) was performed. Constitutional, Topological, and WHIM descriptors, as well as GETAWAY, 3D-MoRSE, and Aromaticity Indices descriptors generated from Dragon, were selected to describe the molecules. The resampling by half-means method was used to detect the outlier molecules. 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-molecular descriptor model was developed on a training set of molecules and the external validation was performed on test set of molecules. 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.