<![CDATA[Purpose: This study aimed to explore the potential of natural compounds as lead candidates for breast cancer therapy through a medicinal chemistry framework using in silico methods, with the goal of identifying safer and more effective alternatives to conventional treatments. Methodology: A qualitative library-based research design was employed using a systematic literature review of 15 primary national and international journal articles published between 2020 and 2025. The computational tools used in the study included AutoDock Vina and PyRx for molecular docking and virtual screening, SwissADME and pkCSM for ADMET predictions, and GROMACS for molecular dynamics simulations. Results: Several natural compounds, including berberine, ellagic acid, camptothecin, and kaempferol, exhibited strong binding affinities toward key breast cancer molecular targets, such as estrogen receptor (ER), progesterone receptor (PR), HER2, and PD-L1. These compounds formed stable ligand–receptor complexes and showed favorable pharmacokinetic properties, conforming to Lipinski’s Rule of Five, thereby suggesting their viability as lead candidates for further development. Conclusions: This study highlights the promising potential of selected natural compounds as lead structures for breast cancer drug development. The integration of molecular docking, pharmacokinetic prediction, and dynamic simulation provides a robust platform for early phase drug discovery. Limitations: The primary limitation of this study is that in silico simulations do not fully capture complex biological interactions within the human body, including metabolic pathways and immune system responses. Contribution: his study contributes to the fields of medicinal chemistry and pharmaceutical sciences by offering updated insights into the utility of natural compounds as anti-breast cancer agents.]]>
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