Anti-breast cancer activity of eleven synthesized molecules was studied using computational approach. The molecular descriptors which describe the anti-breast cancer activities of the studied compounds were optimized using B3LYP/6-31+G* via Spartan 14 and the obtained descriptor were EHOMO (eV), ELUMO (eV), dipole moment (Debye), log P, molecular weight (amu), HBA, HBD, Vol and Ovality. Furthermore, the calculated descriptors played a serious role in developing QSAR model for predicting observed inhibition concentration (IC50) using Gretl for multiple linear regression (MLR) and MATLAB software packages for BPNN. The developed model proved to be effective and predictive; nevertheless, BPNN-QSAR model predicted more efficiently than MLR. Furthermore, molecular docking studied on 1,2,3-triazole-pyrimidine-urea Hybrids and type 3 of 3α-Hydroxysteroid Dehydrogenase (PDB ID: 4xo6) brought about nine conformation each and it was observed that compounds have the highest tendency to inhibit that other studied compounds.