Machine learning-driven discovery of NSC828779 as a multi-mechanistic NLRP3 inflammasome inhibitor for inflammatory...

The NLRP3 inflammasome is a key regulator of the innate immune response and a promising therapeutic target in inflammation-driven diseases. This study aimed to identify potent nature inspired small molecules using AI-guided in silico techniques integrated with NCI-60 high-throughput assays. We developed a machine learning-driven platform that combines pharmacophore modeling, molecular docking, MDS, and RNNs to prioritize candidate compounds. Among these, NSC828779 emerged as a lead scaffold, demonstrating high binding affinity to the ATP-binding site of NLRP3 and superior interaction energy and stability compared to known inhibitors. Docking scores were strongest for NLRP3 (-10.5 kcal/mol), caspase-1 (-8.6 kcal/mol), and ASC (-8.5 kcal/mol), outperforming MCC950, glyburide, and other reference compounds. MDS confirmed the stability of the NLRP3-ASC-caspase-1 complex, supported by RMSD and RMSF analyses showing enhanced conformational integrity. ADMET profiling predicted favorable drug-likeness, solubility, moderate lipophilicity, and low toxicity. Mechanistically, NSC828779 may act as a multi-mechanistic NLRP3 inhibitor by disrupting protein-protein interactions, inhibiting NF-κB signaling, and inducing autophagy. These results establish NSC828779 as a promising candidate for treating inflammation-related disorders and underscore the utility of AI-driven drug discovery platforms in identifying novel inflammasome-targeted therapeutics. Further in vitro and in vivo validation is warranted to support its clinical development.