QSAR-Based Strategies for Selective-HDAC6 Inhibitor Development: A Detailed Review.

Authors

Narges Cheshmazar, Tahereh Chakeri, Zahra Koochaki, Golnoush Rahvar, Siavoush Dastmalchi

Published in

Current medicinal chemistry. Jul 06, 2026. Epub Jul 06, 2026.

Abstract

Histone deacetylase 6 (HDAC6) is a unique class IIb enzyme characterized by dual catalytic domains and predominant cytoplasmic localization, which underlies its involvement in cytoskeletal dynamics, neurodegenerative, and oncogenic pathways. These distinctive features have positioned HDAC6 as an attractive and challenging target for the development of selective inhibitors.
In this review, we critically evaluate reported 2D/3D-QSAR studies of selective HDAC6 inhibitors, including CoMFA, CoMSIA, GRIND, and machine learning-based approaches. Relevant studies were identified through systematic searches of PubMed and Google Scholar between 2003 and 2025.
A comparative analysis of the QSAR literature reveals a consistent structureactivity relationship, including the importance of cap group bulkiness for HDAC6 selectivity, the contribution of linker architecture to potency, and the dominant use of hydroxamic acid as a zinc-binding group, although non-hydroxamic acid gained significant attention.
However, the predictive reliability of current QSAR models remains limited by small and chemically homogeneous datasets, insufficient external validation, and narrow biological endpoints. These limitations partially explain why only a small number of selective HDAC6 inhibitors have progressed into clinical evaluation, where suboptimal pharmacokinetic and druggability profiles remain major barriers.
Overall, this review highlights the need for next-generation QSAR strategies that integrate larger, diverse datasets and structure-informed modeling to support the rational design of clinically viable selective HDAC6 inhibitors.

PMID:
42411215
Bibliographic data and abstract were imported from PubMed on 07 Jul 2026.

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