Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure has been widely studied. Nevertheless, no QSAR studies to predict the activity and identify the required pharmacophore have been developed. In this project, multiple linear regression (MLR) and artificial neural network (ANN) analyses were used to determine the required pharmacophore for telomerase inhibition activity and predicting potency (IC50) of newly designed compounds. A dataset containing 96 compounds were analyzed, and two models were developed from MLR and three models from ANN analyses. The best MLR model has R = 0.90. Errors were calculated using mean percentage error (MPE) criterion, and the best MLR model has MPE of 34% and the best ANN model possesses MPE of 28%. The selected parameters showed that fused phenyl rings or a planer aromatic core, the number of nitrogen and oxygen atoms, having a cationic centre and partial positive charge are essential for describing telomerase inhibitory.