Current Updates on Recent Developments in Artificial Intelligence in QSAR Modelling for Drug Discovery against Lung Cancer

Lung cancer continues to be a leading cause of cancer-related mortality worldwide, underscoring the urgency for innovative and targeted drug discovery strategies. This review critically explores the role of Quantitative Structure-Activity Relationship (QSAR) modelling, particularly its integration with artificial intelligence (AI), in accelerating the identification and optimization of lung cancer therapeutics. Recent progress in multi-target approaches, machine learning (ML) algorithms with mathematical representations, and molecular descriptor engineering has been analyzed, with a special focus on clinical translations. Rather than offering a generic overview, we evaluate how AI-powered QSAR addresses key bottlenecks in drug development, such as data imbalance, model interpretability, and ADMET prediction failures. Notable case studies are examined to highlight translational success stories in lung cancer-specific pathways. This review offers a cohesive synthesis of current advancements, identifies critical gaps and limitations, and proposes future directions for enhancing the real-world applications of QSAR methodologies in oncological drug discovery.