In this study, mTOR is chosen as the main target for breast cancer treatment. While the existing drugs still pose severe side effects, research on finding new anti-cancer drug should be done continuously. Usnic acid (UA) has been studied for its wide range of biological properties and potential in pharmaceutical research.  A structure-based virtual screening approach is applied since it could reduce production time, cost and environmental issues. This study comprises molecular docking simulation, ADMET filtration and drug-likeness prediction. 340 UA derivatives were retrieved from literature and used to build an in-house database. The resulting compounds from docking were then filtered using ADMET prediction which comprises human intestine absorption, aqueous solubility, plasma protein binding, cytochrome P450 2D6 (CYP2D6) and hepatotoxicity parameters to identify the most potent UA derivatives with favourable physicochemical characteristics. After all, the hit compound, 118, was further stimulated in order to forecast its drug-like features. The chalcone-based scaffold of 118 resembled the reported breast cancer compound’s chemical structure strengthening the results obtained from this study. Thus, it is concluded that the structure-based virtual screening was an efficient and effective approach in the discovery of UA derivative, 118, as a potential novel mTOR inhibitor to treat breast cancer.