Generative AI Uncovers Novel Chrebp/Txnip Axis Inhibitors with Potential Anti-inflammatory Activity

Type 2 diabetes is driven in part by metabolic inflammation, where activation of the Chrebp/Txnip axis promotes NLRP3 inflammasome assembly, leading to pancreatic β-cell dysfunction and pro-inflammatory cytokine release. Despite the therapeutic relevance of this pathway, the Chrebp-14-3-3 (regulatory-protein client) protein–protein interaction (PPI) remains largely underexplored, with only a limited number of small-molecule modulators reported to date. To address this gap, we developed an artificial intelligence-driven generative design framework for de novo discovery of selective PPI-targeting compounds. A conditional recurrent neural network (cRNN), implemented as a quantitative structure–property relationship-guided generative network (QSPR-GEN), was pretrained on a large, chemically diverse corpus to learn general SMILES syntax and structural priors, and subsequently fine-tuned on a curated, target-focused data set of approximately 5900 compounds, achieving high scaffold uniqueness (94.6%). Selectivity-oriented physicochemical descriptors were incorporated as conditional inputs to bias generation away from promiscuous chemotypes, while maintaining anchoring to a known active seed. Structure-based refinement was further applied by focusing on the noncanonical α-helical epitope unique to the Chrebp regulatory-protein interface, establishing a dual-layered strategy for selective PPI modulation. The integrated pipeline, combining virtual screening, molecular dynamics simulations, and MM/PBSA free-energy calculations, prioritized lead candidates with favorable binding energetics and pharmacokinetic profiles. In THP-1 macrophages under metabolic stress, the top candidate T7 markedly suppressed Txnip and NLRP3 expression, reduced IL-1β secretion, and attenuated pyroptotic cell death, outperforming a reference inhibitor. Collectively, this study presents a robust computational framework for the inverse design of challenging PPIs and demonstrates its utility through the identification and experimental validation of mechanistically precise lead compounds, exemplified by T2 and T7.