Authors: Yasser Fakri Mustafa
Source: Next Materials (article, 2026-04-09)
Field: Chemistry — Inorganic Chemistry

Generative chemistry for coumarins: Redesigning classical scaffolds through AI-driven molecular innovation Coumarins are classical heterocyclic scaffolds with wide-ranging photophysical and biological activities. Despite decades of research, their structural diversity and translational potential remain underexplored. Advances in artificial intelligence (AI) and generative chemistry now offer transformative opportunities to redesign coumarin cores, expand chemical space, and accelerate discovery. To critically synthesize current advances in AI-guided coumarin design, highlight computational–experimental workflows enabling scaffold diversification, and discuss challenges and future perspectives for generative chemistry in coumarin research, with a particular emphasis on integrating computational–experimental pipelines, assessing scaffold novelty, and identifying underexplored structural motifs that have not been systematically covered in previous reviews. The review analyzes state-of-the-art approaches including molecular encoding (SMILES, graph-based, 3D descriptors), variational autoencoders, reinforcement learning, de novo design engines, docking–QSAR pipelines, retrosynthetic planning tools, and ADMET prediction frameworks. Representative case studies illustrate redesign strategies for photophysical tuning, target engagement, pharmacokinetics, and toxicological refinement. AI-driven workflows successfully generate novel coumarin-like structures with tailored photophysical features, enhanced biological selectivity, and improved ADMET characteristics. Across reported studies, predictive models reported in individual studies to achieve validation accuracies or equivalent performance metrics range approximately 75–85%, demonstrating reliable structure–property and structure–activity prediction. Integrative pipelines combining docking, QSAR, retrosynthesis, and iterative laboratory feedback effectively guide compound prioritization and synthesis. This review synthesizes cross-disciplinary methodologies, identifies gaps in coumarin-specific datasets, and highlights emerging AI strategies that have not been systematically collated. However, model biases, limited coumarin-specific training data, interpretability constraints, and reproducibility challenges remain substantial barriers. Generative chemistry provides a powerful complement to classical medicinal chemistry, enabling the rapid exploration of new coumarin scaffolds and functional properties. Importantly, this review provides a novel, integrative perspective by connecting AI-driven molecular generation, experimental validation, and scaffold-level innovation specifically for coumarins, offering insights not previously consolidated in the literature. Continued progress in data curation, model interpretability, and experimental integration could be essential to fully realize AI’s potential in advancing coumarin-based therapeutic and materials innovation. • AI-driven generative chemistry expands the structural diversity of classical coumarins. • Integrated docking–QSAR pipelines accelerate coumarin design and prioritization. • Generative models reveal new structure–property relationships in coumarins. • AI reshapes coumarin photophysics, bioactivity, and ADMET optimization strategies. • Unified design–make–test–learn workflows enhance coumarin discovery efficiency. • Data curation and model interpretability remain key challenges in coumarin AI design. Generative grammar ; Field (mathematics) ; Generative model Asymmetric Hydrogenation and Catalysis ; Cyclization and Aryne Chemistry ; Computational Drug Discovery Methods