A machine learning-Assisted QSAR and integrative computational combined with network pharmacology approach for rational identification of tankyrase inhibitors in colon adenocarcinoma

The dysregulation of the Wnt/β-catenin signaling pathway serves as a central driver of Colorectal cancer (CRC) initiation and progression. Tankyrase (TNKS), a PARP family enzyme, facilitates Wnt-driven tumorigenesis by PARylating and destabilizing Axin, thereby promoting β-catenin accumulation. In APC-mutated CRC, TNKS inhibition restores β-catenin degradation, highlighting its potential as a therapeutic target. To address this gap, an integrative QSAR model was constructed to identify novel TNKS inhibitors with favorable pharmacokinetic and therapeutic efficacy. A dataset of 1100 TNKS inhibitors was retrieved from the ChEMBL database and subjected to ligand-based QSAR modeling to predict potent chemical scaffolds based on 2D and 3D structural and physicochemical molecular descriptors. To enhance model reliability, Machine learning (ML) approaches such as feature selection and random forest (RF) classification models were applied. The models were trained, optimized, and rigorously validated using internal (cross-validation) and external test sets, achieving a high predictive performance (ROC-AUC) of 0.98. Virtual screening of prioritized candidates was complemented by molecular docking, dynamic simulation, and principal component analysis to evaluate binding affinity, complex stability, and conformational landscapes. This strategy led to the identification of a potential TNKS inhibitor, Q1 (Olaparib), as a possible repurposed drug against TNKS. Network pharmacology further contextualized TNKS within CRC biology, mapping disease-gene interactions and functional enrichment to uncover TNKS-associated roles in oncogenic pathways. Collectively, these findings underscore the effectiveness of combining machine learning and system biology to accelerate rational drug discovery. Olaparib emerges as a promising candidate for TNKS-targeted therapy, providing a strong computational foundation for experimental validation and future preclinical drug development.