Tracking protein kinase targeting advances: integrating QSAR into machine learning for kinase-targeted drug discovery

Protein kinases are vital drug targets, yet designing selective inhibitors is challenging, compounded by resistance and kinome complexity. This review explores Quantitative Structure-Activity Relationship (QSAR) modeling for kinase drug discovery, focusing on integrating traditional QSAR with machine learning (ML)-CNNs, RNNs-and structural data. Methods include structural databases, docking, and deep learning QSAR. Key findings show ML-integrated QSAR significantly improves selective inhibitor design for CDKs, JAKs, PIM kinases. The IDG-DREAM challenge exemplifies ML's potential for accurate kinase-inhibitor interaction prediction, outperforming traditional methods and enabling inhibitors with enhanced selectivity, efficacy, and resistance mitigation. QSAR combined with advanced computation and experimental data accelerates kinase drug discovery, offering transformative precision medicine potential. This review highlights deep learning-enhanced QSAR's novelty in automating feature extraction and capturing complex relationships, surpassing traditional QSAR, while emphasizing interpretability and experimental validation for clinical translation.