Background: Background: Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.Objective: In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors. Methods: The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q2), noncross- validated correlation coefficient (r2ncv) and predicted correlation coefficient (r2pred) of the training and test set molecules, respectively. Results: The CoMFA (q2, 0.670; r2ncv, 0.930; r2pred, 0.968), CoMFA-RF (q2, 0.694; r2ncr, 0.926; r2pred, 0.927), CoMSIA (q2, 0.834; r2ncv, 0.956; r2pred, 0.958) and HQSAR models (q2, 0.854; r2ncv, 0.900; r2pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings. Conclusion: These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.