Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models.

Cancer is a life-threatening disease that can attack humans at any part of the body as a consequence of abnormal cell growth and proliferation, leading to tumors that can be fatal. Breast cancer is one of the deadliest ailments in the world after lung cancer. Through hormonal and genetic changes that occur in DNA, breast cancer can affect women. The quantitative structural-property relationship (QSPR) is used to provide a comprehensive study of 16 drugs involved in the treatment of breast cancer. According to their chemical structure, the drugs being studied are modeled as molecular graphs. The purpose of this study is to examine the utility of new entire neighborhood topological indices in characterizing the physicochemical properties of a range of breast cancer drugs. Cubic regression analysis was initially employed, followed by multiple linear regression modeling to enhance the correlation between the entire neighborhood topological indices and some properties of the aforementioned drugs. The analysis results are presented and discussed, leading to conclusions about the potential of these new indices for pharmaceutical and chemical research on breast cancer treatments.