Multiscale insights into cornusideʼs effects on NAFLD: A cross-disciplinary integrating bioinformatics, computational chemistry, and machine learning

Abstract

Background

Non-alcoholic fatty liver disease (NAFLD) is a complex metabolic disorder involving intertwined signaling pathways, posing challenges for targeted therapeutic interventions. Cornus Fructus (CF), a traditional medicinal herb, holds potential for NAFLD treatment, with cornuside (COR) identified as its primary active component.

Methods

This study employed a cross-disciplinary approach, integrating bioinformatics, computational chemistry, and machine learning to uncover COR’s therapeutic mechanisms with precision and depth.

Results

Using bioinformatics-driven analysis, 27 core targets were identified, revealing that COR modulated critical metabolic and inflammatory pathways. COR mitigated insulin resistance by regulating the AKT/GSK3β axis, enhanced cholesterol metabolism through LXR signaling, promoted fatty acid oxidation via PPARα activation, and suppressed inflammation by inhibiting NF-κB signaling. These results highlighted COR’s ability to orchestrate multi-pathway regulation essential for restoring metabolic homeostasis in NAFLD. Molecular docking and molecular dynamics (MD) simulations provided atomistic insights, demonstrating COR’s stable and high-affinity interactions with key targets. Additionally, machine learning algorithms enhanced target identification and pathway prediction, improving the precision and efficiency of the discovery process.

Conclusion

This study offered multi-scale mechanistic insights into COR’s therapeutic effects on NAFLD, bridging experimental pharmacology and computational methods. The integration of bioinformatics, molecular simulation, and machine learning established a comprehensive framework for drug discovery, positioning COR as a promising candidate for NAFLD therapy and guiding future development of precision interventions.