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

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Outperformed reference inhibitor (specific quantitative data not provided)

• Primary Outcome: T7 compound markedly suppressed Txnip and NLRP3 expression, reduced IL-1β secretion, and attenuated pyroptotic cell death

• Nnt Or Sensitivity: 94.6% scaffold uniqueness in generated compounds

• Confidence Interval: Not reported

Clinical Application

Very early stage - requires extensive preclinical development, safety studies, and clinical trials before any therapeutic application Would require TGA approval pathway for novel chemical entities; potential relevance to Australian diabetes burden but years from clinical application Patients with type 2 diabetes and metabolic inflammation (theoretical future application)

Abstract

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.

References

1. 1.Qayyum, N., Khan, A. W., Haseeb, M., Ahmed, B., Farooq, M., Manan, A., Kim, E., & Choi, S. (2026). Generative AI uncovers novel Chrebp/Txnip axis inhibitors with potential anti-inflammatory activity. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.5c02172