Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients

Abstract Hepatocellular carcinoma (HCC) remains a major global health challenge due to its molecular heterogeneity, late diagnosis, and limited therapeutic options. Recent studies have identified isonicotinylation (K inic ), a novel lysine acylation, as a regulatory modification influencing carcinogenic protein activity and liver cancer progression. In this study, we established the K inic Index (K inic I), an artificial intelligence (AI)-driven predictive model that integrates multi-omics data and consensus clustering to classify HCC patients into two distinct K inic subgroups. Patients in the high-K inic subgroup exhibited significantly worse overall survival, demonstrating the value of K inic I for risk stratification and outcome prediction. Machine learning approaches (LASSO, RSF) coupled with Shapley additive explanation (SHAP) analysis identified CYP2C9 and G6PD as the most influential prognostic variables associated with HCC progression. Single-cell and spatial transcriptomic analyses confirmed that CYP2C9 and G6PD are primarily localized in malignant hepatocytes with high metastatic potential, underscoring their clinical relevance. Importantly, using the GraphBAN deep learning framework and ADMET-AI screening, we prioritized candidate compounds targeting CYP2C9 and G6PD, followed by molecular docking that validated strong binding affinities, suggesting their potential as novel therapeutics. Together, our study demonstrates that K inic I is a powerful AI-enabled platform for prognostic modeling, molecular stratification, and multitarget drug discovery, providing a foundation for precision oncology and resistance-aware treatment strategies in HCC patients.