Abstract
INTRODUCTION: Breast cancer, one of the most prevalent malignancies in women begins in the milk ducts or lobules and is divided into invasive and non-invasive variants. The kind stage and molecular features of the cancer determine the treatment strategy which may include surgery, chemotherapy, and targeted drugs. Early identification through screening is critical to increasing patient survival rates. METHODS: In this study, we look at the efficacy of numerous breast cancer drugs, including Toremifene, Tucatinib, Ribociclib, Olaparib, Abemaciclib, Anastrozole, Letrozole, Thiotepa, Tamoxifen, and Megestrol Acetate. We investigate their chemical and physical properties, including molar volume (MV), polarizability (P), molar refractivity (MR), polar surface area (PSA), and surface tension (ST). We employ Quantitative Structure Property Relationship (QSPR) analytical approaches, including curvilinear regression and multiple linear regression (MLR), to model and predict the physicochemical properties of these medications by analyzing the impact of molecular descriptors on these properties. RESULTS: A comparison of the two regression techniques is done to see how accurate their predictions are and to find the best way to model the data. Furthermore, resolving topological indices examines the relationship between molecular structure and therapeutic effectiveness. DISCUSSION: The outcomes of these studies help to further our understanding of breast cancer treatments and the development of more focused and customized therapeutics.