Authors
Harshada Rajendra Bafna, Nitin Rajendra Shirsath
Published in
Mini reviews in medicinal chemistry. Sep 09, 2026. Epub Sep 09, 2026.
Abstract
In medicinal chemistry, scaffold hopping has become a crucial strategy for identifying new lead molecules with improved physicochemical, pharmacological, and intellectual property profiles. To underline its importance in modern drug discovery and lead optimization, this study was conducted.
To gather data on scaffold-hopping techniques and their role in drug discovery, a thorough review of the published literature was conducted. Relevant scientific publications, reviews, and computational studies were methodically reviewed to categorize scaffold-hopping techniques, such as pharmacophore-based techniques, formalized scaffolds, ligand-based techniques, fragment and fingerprint-based techniques, scaffold abstraction, and biological similarity-based techniques.
The effectiveness of lead optimization, virtual screening, and scaffold identification has been significantly enhanced by the incorporation of artificial intelligence and machine learning. Successful applications have also been reported in the advancement of therapeutics for CNS disorders, diabetes mellitus, cancer, and inflammatory diseases.
The results highlight the critical role scaffold hopping plays in drug development by facilitating the creation of novel molecular frameworks with enhanced potency, pharmacokinetic characteristics, and patentability. Scaffold hopping continues to develop as a potent platform for increasing chemical diversity and meeting unmet medicinal needs, despite challenges related to synthetic feasibility, predictive accuracy, and experimental validation.
The review illustrates its important contribution to contemporary medicinal chemistry by methodically describing its classifications, approaches, new computational techniques, and variety of therapeutic uses. From this study, we conclude that scaffold hopping is one of the most effective methods for discovering new drugs.
PMID:
42736662
Bibliographic data and abstract were imported from PubMed on 15 Sep 2026.
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