Critical structural features of HDAC11 inhibitors.

Authors

Indrasis Dasgupta, Srijoni Sinha, Shovanlal Gayen

Published in

Methods in enzymology. Volume 734. Pages 173-198. Epub Jun 09, 2026.

Abstract

Histone deacetylase 11 (HDAC11), the sole member of the class IV HDACs, has gained significance as a key regulator of a wide range of physiological and pathological processes. The structural characterization of HDAC11 remains challenging due to the absence of an experimentally determined crystal structure. Proper analysis of reported HDAC11 inhibitors can yield valuable insights into the key structural fingerprints essential for inhibition. Using a diverse range of descriptor sets, including various 2D descriptors and fingerprint-based descriptors, it becomes possible to effectively identify critical structural features associated with HDAC11 inhibitory activity. These fingerprints enable the recognition of substructural features that play an important role in binding within the HDAC11 catalytic site and in achieving selective inhibition. In the present chapter, various computational methodologies, including Bayesian classification, Recursive partitioning approaches and machine learning-based classification models, have been discussed in a user-friendly manner to identify the critical structural features required for effective HDAC11 inhibition. The insights derived from these approaches are expected to assist researchers in the rational design of selective HDAC11 inhibitors, which may be further validated through experimental studies.

PMID:
42692754
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

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