Applications of artificial intelligence in the synthesis, docking, and pharmacological profiling of coumarins

Coumarins, a diverse class of benzopyrone derivatives, have long captivated researchers due to their broad spectrum of pharmacological activities and synthetic versatility. In recent years, the convergence of artificial intelligence (AI) with pharmaceutical sciences has redefined how researchers approach the synthesis, molecular docking, and pharmacological profiling of such bioactive compounds. This review explores the transformative potential of AI in the context of coumarin research, presenting a holistic view of how machine learning algorithms, deep learning models, and data-driven design strategies are reshaping drug discovery. Traditional synthesis of coumarins, often constrained by multistep protocols and environmental concerns, is now being revolutionized through AI-assisted reaction predictions and retrosynthetic analyses. AI enables the generation of synthetically accessible molecules with optimized structural features, significantly reducing time and resource investment. Furthermore, molecular docking, critical to understanding structure-activity relationships, is increasingly benefiting from AI-enhanced scoring functions and predictive modeling, thus improving the accuracy of ligand-receptor interaction predictions. Pharmacological profiling, both in vitro and in vivo, is becoming more streamlined with AI models capable of predicting bioactivity, toxicity, and pharmacokinetics, making the lead optimization process more efficient and reliable. Public databases, curated datasets, and integrative cheminformatics platforms now provide a rich foundation for data mining and drug-target interaction studies. This review not only highlights the successes of AI in coumarin-based drug design but also discusses existing challenges, including algorithm interpretability, data quality, and regulatory considerations. Ultimately, the synergy between AI and coumarin research presents an exciting frontier that holds immense promise for accelerating drug discovery and advancing personalized therapeutics.