Rational drug design, synthetic and artificial intelligence approaches for bioactive heterocycles: advances and perspectives.
TL;DR
This review covers advances in designing and synthesizing bioactive heterocycles, highlighting structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches with computational modeling and Artificial Intelligence (AI) to find potent, selective molecules with good Absorption, Distribution, Metabolism, Excretion and Toxicity profiles.
Abstract
Heterocyclic scaffolds are vital to medicinal chemistry due to their versatility, diversity, and ability to target various biological molecules. This review covers advances in designing and synthesizing bioactive heterocycles, highlighting structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches with computational modeling and Artificial Intelligence (AI) to find potent, selective molecules with good Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) profiles. Case studies show the successful development of heterocyclic drugs for cancer, microbial infections, inflammation, viral infections, and Central Nervous System (CNS) disorders. Synthetic methods have evolved from classical electrophilic/nucleophilic reactions to modern techniques like multicomponent reactions, microwave synthesis, metal catalysis, and green chemistry, making frameworks more accessible. The review discusses Quantitative Structure-Activity Relationship (QSAR) studies for molecular optimization. Challenges like synthetic complexity and resistance remain, but emerging trends like machine learning, omics, and enzyme synthesis offer new opportunities. Ultimately, combining design principles and innovative methods can speed up drug discovery and enable sustainable, personalized therapies with heterocyclic pharmacophores.