Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{210222,
        author = {Dr. Rupak Roy and Panshul Purusharthy},
        title = {Structure-Based Drug Discovery for Breast Cancer: A Review of Modern Computational Approaches and Future Perspectives},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {263-268},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210222},
        abstract = {Breast cancer is a heterogeneous disease in which differences in molecular characteristics, treatment response, drug resistance, and recurrence continue to complicate therapeutic development. Computational biology has become an important component of modern drug discovery by enabling the prediction and analysis of molecular interactions before extensive laboratory testing. This review summarizes the role of structure-based drug discovery (SBDD) and related computational approaches in breast cancer research, with emphasis on molecular docking, virtual screening, molecular dynamics simulations, quantitative structure–activity relationship analysis, fragment-based drug discovery, drug-likeness assessment, and ADMET profiling. The review also discusses the increasing integration of artificial intelligence and machine learning with structural biology and cheminformatics for candidate identification, molecular optimization, diagnosis, prognosis, and personalized treatment. It also highlights the relationship between computational prediction and experimental validation, as computational models still struggle to reproduce the dynamic, heterogeneous nature of biological systems. Overall, integrating computational methods with structural biology, artificial intelligence, and laboratory validation may improve the efficiency, selectivity, and personalization of future breast cancer drug development.},
        keywords = {breast cancer; structure-based drug discovery; molecular docking; molecular dynamics; artificial intelligence; ADMET; personalized medicine; computational drug discovery},
        month = {November},
        }