Network Pharmacology and Molecular Docking-Based Investigation of Herbal Compounds Against Cancer-Related Signaling Pathways
Keywords:
network pharmacology, molecular docking, cancer signaling, herbal compounds, systems architecture, reproducibility, governance, fairnessAbstract
Cancer is a heterogeneous family of diseases characterized by the progressive dysregulation of intracellular and intercellular signaling networks. The limitations of single-target therapeutic strategies, including acquired resistance and systemic toxicity, have motivated the search for multicomponent interventions that can modulate multiple nodes within cancer-related pathways. Herbal medicines represent a rich source of structurally diverse compounds with potential polypharmacological activity, yet their mechanistic complexity poses substantial challenges for evidence-based investigation. This paper examines the integration of network pharmacology and molecular docking as a systems-level computational framework for investigating herbal compounds against cancer-related signaling pathways. The discussion is oriented toward architectural design, data integration, computational trade-offs, validation robustness, reproducibility, governance, and translational sustainability. Network pharmacology provides a high-level topological representation of compound-target-disease relationships, while molecular docking contributes structural filters for evaluating binding plausibility. The combined pipeline enables prioritization of herbal constituents for experimental and clinical follow-up, but it also introduces systemic vulnerabilities related to data bias, pathway incompleteness, scoring function uncertainty, and regulatory ambiguity. This paper argues that the utility of such platforms depends not only on algorithmic accuracy but also on transparent data provenance, interoperable infrastructure, rigorous external validation, and ethically aligned deployment. Future developments should emphasize adaptive learning architectures, federated data governance, cross-domain knowledge graphs, and robust frameworks for translating computational predictions into sustainable and equitable cancer therapeutic research.
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