10.22376/ijpbs.2019.10.1.p1-12 Volume 14 Issue 3 July-September 2023 Comprehensive preponderance of breast cancer and its high recurrence attracts the attention of many research programs in drug discovery. Several statistics are used to evaluate its occurrence. The discovery of new and more efficient anticancer agents is a key area in chemotherapy. A Quantitative-Structure-Activity Relationship (QSAR) study was performed on 4, 6-diaryl-2-pyrimidinamine derivatives; several highly descriptive and predictive QSAR models for these compounds were obtained. In the present study, QSAR studies have been performed on 31 compounds of 4, 6-diaryl-2-pyrimidinamine derivatives as antibreast cancer agents using CHEM sketch and NCSS Software. The software includes; ChemDraw version 12.0.2, Spartan'14 (version 1.1.2), Material Studio (V8) software, Pyrex software, PADEL V2.20, DTC data lab software version, and Auto Dock Visualizer version 4.2. QSAR models have been developed by using multiple linear regressions to identify descriptors, which focus on biological activity, Leave One out Method was employed in cross-validation analysis to validate the developed model and has indicated that activity can be best modeled in multi-parametric regression). Upon applying the Leave One out (LOO) method, one compound which exists as an outlier leads to qualitative findings with a high degree of statistical significance and good predictive ability. Neha Sharma, Vipin Saini Shivani Sharma, Sushma Gautam, Jyoti Sharma, Vipra Kohli, Munsaka Siankuku, Monika Sen, Sonali Bhanbhani, Surbhi Chopra, Aditi Dwivedi and Satinder Singh Minhas Breast cancer, Drug discovery, Chemotherapy, Regression analysis, Leave One Out, Cross-validation analysis 6-20