Artificial Intelligence–assisted Drug Design in Heterocyclic Chemistry: A Fragment Based Machine Learning Review

Heterocyclic compounds form the backbone of a majority of clinically approved smallmolecule drugs due to their structural diversity, favorable physicochemical properties, and wide spectrum of biological activities. Despite their importance, conventional heterocyclic drug discovery remains a time-consuming and resourceintensive process, relying heavily on trial-and-error synthesis and extensive biological screening. In recent years, artificial intelligence (AI), particularly machine learning (ML), has emerged as a transformative approach in drug discovery, enabling rapid prediction of biological activity, toxicity, and pharmacokinetic behavior. This review critically examines the role of AI-assisted and fragment-based machine learning strategies in heterocyclic drug design. Special emphasis is placed on heterocycle-aware fragment representations, structure–activity relationship modeling, attention-based learning, and model interpretability. Current challenges, limitations, and future prospects of AI-driven heterocyclic medicinal chemistry are also discussed, highlighting the potential of these approaches to accelerate rational drug design and reduce attrition rates.