Artificial Intelligence-Driven Discovery of Coumarin-Based Therapeutics: Revolutionizing Target Identification and Validation

Artificial intelligence (AI) has transformed modern drug discovery by reshaping how therapeutic targets are identified, validated, and optimized. Coumarin derivatives, with their diverse pharmacological activities and structural adaptability, offer a rich chemical space for AI-guided exploration. However, despite the potential of integrating AI methodologies with coumarin chemistry to accelerate the identification of novel disease targets and design safer, more efficient drug candidates, a unified synthesis of these data-driven workflows remains lacking. This review aims to address this research gap by compiling and analyzing literature from the past ten years to provide a comprehensive content framework. The objective is to evaluate data-driven approaches—ranging from literature-based data mining, molecular docking, and predictive modeling to deep-learning frameworks and multiomics integration—that collectively enhance coumarin-target discovery. Emphasis is placed on AI-enabled workflows that connect structural, functional, and phenotypic data to support hypothesis generation, target prioritization, and validation across computational and experimental domains. Recent studies demonstrate that AI-assisted algorithms can accurately predict coumarin–protein interactions, uncover unrecognized biological targets, and rationalize structure–activity relationships. Deep-learning and risk–benefit models have improved target ranking, while multiomics data fusion has revealed disease-specific mechanisms in oncology, metabolic, infectious, and cardiovascular disorders. These insights have translated into tangible outcomes, such as the design of novel coumarin–quinone hybrids and selective enzyme inhibitors. The convergence of AI and coumarin-based medicinal chemistry heralds a paradigm shift in therapeutic target identification. Future research directions and prospects should focus on ethical data governance, interpretability, and cross-disciplinary collaboration to position AI-driven coumarin research at the forefront of next-generation precision therapeutics. • AI accelerates the discovery of therapeutic targets for diverse coumarin derivatives. • Integrated data ecosystems enhance coumarin target prediction and prioritization. • Knowledge graphs reveal coumarin–target networks across biological scales. • Deep learning refines coumarin target hypotheses with greater translational value. • AI-driven workflows reduce risk and shorten timelines in coumarin drug discovery.