Cancer remains a major global health challenge, with drug resistance posing a significant barrier to effective treatment. This study explores the potential of bioinformatics and computational chemistry, specifically molecular docking and Density Functional Theory (DFT), in elucidating drug-protein interactions and uncovering mechanisms of resistance in cancer therapy. Bioinformatics analyses were conducted to identify overexpressed genes in breast cancer using the UALCAN web portal, followed by functional annotation through the DAVID tool. Computational chemistry methods, including DFT, were employed to model and optimize the electronic structures of potential drug candidates. Molecular docking simulations predicted the binding affinity between these drugs and overexpressed proteins, focusing on the HER2/ERBB2 gene, a key player in breast cancer progression and resistance. The integration of bioinformatics and computational chemistry provided comprehensive insights into drug-protein interactions, revealing critical pathways involved in cancer resistance. Our findings underscore the utility of combining these disciplines to discover new therapeutic targets and improve personalized treatment strategies, particularly for HER2-positive breast cancer. The detailed interaction analysis between the HER2 protein and the TAK-285 drug highlights the robustness and specificity of the binding interactions, offering a promising avenue for the rational design of more effective HER2 inhibitors.