Abstract: A QSAR approach on a dataset of 546 inhibitors of telomerase activity, by interaction with G-quadruplex DNA, was carried out using 2D autocorrelation descriptors. A linear discriminant analysis (LDA) was made on a training set of 437 compounds and it was assessed with 109 compounds belongs to the test set. The model good classified the 80.09% of the dataset and the percentage of good prediction was 78.89%. Only 5 compounds were not classified. The 2D autocorrelation descriptors are able to explain the factors that stabilize the G-quadruplex structure and consequently the inhibition of telomerase by this biological mechanism.