Leveraging Data-Driven AI to Explore Plant-Based Compounds for Neuroinflammation and Inflammatory Receptor Modulation via Polypharmacology

Polypharmacology has emerged as a promising approach in modern drug discovery, allowing researchers to address the complex interplay of multiple biological pathways underlying disease mechanisms. In this study, advanced artificial intelligence (AI)–based tools were applied to evaluate the therapeutic potential of Cyamopsis tetragonoloba, a medicinal plant traditionally recognized for its anti-inflammatory activity, in the context of inflammation and neuroinflammation. By integrating AI-driven network pharmacology with protein–protein interaction analyses, AKT1 (protein kinase B alpha) was identified as a pivotal target that mediates the plant’s pharmacological effects. This finding suggests a central role of AKT1 in modulating inflammatory signaling pathways.

To further explore these insights, molecular docking experiments were conducted using bioactive constituents derived from Berberis aristata, another well-documented medicinal plant with anti-inflammatory relevance. The docking studies focused on critical proteins involved in inflammatory processes, including AKT1, PTGS2, STAT3, PPARG, SIRT1, and MAPK1. Several compounds demonstrated strong binding affinities, in some cases comparable to or surpassing those of clinically approved anti-inflammatory drugs. Such results not only validate the computational predictions but also point to the potential of these plant-derived molecules as viable therapeutic candidates.

Overall, the findings highlight the value of combining AI technologies with polypharmacological strategies to accelerate the discovery of plant-based therapies. By identifying key molecular targets and validating compound interactions, this integrated approach offers new opportunities for developing effective treatments for inflammatory and neuroinflammatory disorders, while bridging traditional medicinal knowledge with modern computational drug discovery.