NLRP3 inhibitors(Smith, Gambrell & Russell)

NOD-Like Receptor Protein-3 (NLRP3) inflammasome emerged as a crucial therapeutic target in epilepsy, playing a significant role in regulating inflammation associated with various neurological conditions. Despite its biological importance, the development of potent NLRP3 inhibitors for epilepsy therapy remains limited. This study establishes a machine-learning (ML)-based QSAR framework to evaluate Jeevaneeya Rasayana phytochemicals as potential NLRP3 inhibitors in epilepsy, integrating traditional Ayurvedic insights with extensive regression benchmarking and rigorous validation strategies. SVR, NuSVR, LGBMR, and HGBR regression algorithms were identified as the top four models by the Lazy Predict Python library and subsequently refined using scikit-learn. The SVR model demonstrated reliable predictive power, showing superior internal validation (high q² = 0.679, r²_training = 0.797, and a lower RMSE = 0.454) and fully met Golbraikh and Tropsha's criteria for external validation. Model robustness was further confirmed through Y-randomization and applicability domain analysis. The SVR model accurately predicts pIC₅₀ values and identifies seven active Jeevaneeya Rasayana BBB-permeable phytocompounds. Molecular docking pinpointed 4',7-dihydroxy-3'-methylflavone as a promising lead with favorable binding affinity (-7.011 kcal/mol), supported by ADMET and drug-likeness evaluation. Furthermore, the structural stability and compactness of the 4',7-dihydroxy-3'-methylflavone-NLRP3 complex were validated through 500 ns molecular dynamics (MD) simulations, corroborated by PCA, FEL, and MMGBSA analyses, while density functional theory (DFT) calculations confirmed its electronic and chemical stability. Collectively, these integrated computational findings highlight 4',7-dihydroxy-3'-methylflavone as a promising scaffold for rational drug design targeting NLRP3 and provide a strong foundation for further optimization of NLRP3 inhibitors for epilepsy-associated neuroinflammation.