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

Abstract

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.