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{195027,
        author = {K. Sakthyi and Prabhakar Singh and Saqib Hassan},
        title = {Network Pharmacology and Machine Learning Approaches for Repurposing Phytochemicals in ER+ and HER2+ Breast Cancer: From Target Prediction to Clinical Potential},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5944-5952},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195027},
        abstract = {ER+/HER2+ breast cancer is a difficult form of breast cancer, characterized by ER and HER2 signaling. These pathways often exhibit crosstalk, leading to PI3K/AKT/mTOR pathway activation and therapy resistance. The available treatment options face the problems of drug resistance, cancer stem cells, and side effects. Network pharmacology, a technique, and ma-chine learning, a tool, can be employed to identify mul-ti-target phytochemicals. The use of tools like TCMSP, STRING, and Cytoscape, along with ML, can assist in the identification of potential phytochemicals, which can be represented by compounds like genistein, thy-moquinone, epigallocatechin gallate, naringenin, hes-peretin, and resveratrol. These compounds can modu-late important pathways. The review article emphasizes the use of phytochemicals as a multi-target therapeutic strategy.},
        keywords = {Machine learning, Molecular docking, multi target therapy, Network pharmacology, Phytochemicals.},
        month = {March},
        }