Computational Medicinal Chemistry‐Based Drug Repurposing and Bioactivity Optimization for Rare and Emerging Diseases: A Critical Review of 2021–2026 Landscape

ABSTRACT This review examines how computational medicinal chemistry has transformed drug repurposing and bioactivity optimization for rare and emerging diseases over the 2021–2026 period. Conventional de novo drug discovery remains costly and slow typically requiring more than one billion dollars and 10–17 years failing to adequately serve the over 300 million people living with rare diseases, 95% of whom lack approved treatments. Drug repurposing offers an attractive alternative by leveraging established safety, pharmacokinetic, and manufacturing profiles. Computational approaches are integral to this strategy: target‐based methods (e.g., molecular docking) supply mechanistic insights; ligand‐based methods (e.g., QSAR) enable screening without solved target structures; network‐based approaches integrate multi‐omics data; and artificial intelligence enhances predictive accuracy across all three domains. Key developments include pharmacological chaperones for Fabry disease, allosteric regulators for mucopolysaccharidosis type I, and computationally nominated COVID‐19 therapeutics. Major challenges include inconsistent datasets, limited docking accuracy, qualitative structure–activity relationship (QSAR) overfitting, and a persistent translational gap. Closing this validation gap through data standardization, rigorous prospective testing, and multidisciplinary collaboration is essential if computational predictions are to yield safe, effective therapies for unmet clinical needs.