<![CDATA[Purpose: This study describes the shift in modern drug discovery toward a computational systems-based paradigm, emphasizing multi-target molecular docking as a key strategy to unravel complex molecular interactions in biological systems. Methods: A systematic literature review was conducted using publications from 2020 to 2025 retrieved from the PubMed, Scopus, ScienceDirect, and MDPI databases. Results/findings: The analysis demonstrates that integrating molecular docking, Molecular Dynamics (MD) simulations, and network pharmacology enhances polypharmacology and drug repurposing strategies for complex diseases, such as diabetes, Alzheimer's, and viral infections. Bioactive compounds, including quercetin, luteolin, kaempferol, diosgenin, ?-amyrenone, and copper (II) complexes, target critical biological pathways (AGE–RAGE, NF-?B, STAT3–CASP3–HIF1A) and essential viral proteins. Conclusions: The integration of multi-target molecular docking, network pharmacology, and AI-based drug design forms a new paradigm in modern drug discovery. This approach enables a systemic analysis of ligand–protein interactions, accelerates the identification of therapeutic targets, and improves the accuracy and efficiency of virtual screening. The combination of these three approaches strengthens the direction towards computational systems pharmacology, which supports data-driven and sustainable drug design. Limitations: This study is based solely on existing computational data, without experimental validation to confirm the predicted interactions. Contributions: This study highlights the integrative potential of multi-target molecular docking and network pharmacology as a bridge between computational prediction and experimental pharmacology. It offers a conceptual foundation for AI-assisted drug design and encourages future research on experimental validation and predictive modeling to optimize multitarget therapies.]]>
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