Histone deacetylase 8 (HDAC8) inhibitors play a pivotal role in epigenetic regulation. Numerous HDAC8 inhibitors (HDAC8is), that are non-hydroxamates have been identified to date, and a few of them exhibit antiproliferative activity that is on par with hydroxamates. While many non-hydroxamate-based HDAC8is have demonstrated selectivity, hydroxamate-based HDAC8is, like Vorinostat and TSA, have a tendency of non-specificity among the different HDAC isoforms. Moreover, because of the unfavorable toxic side effects, there are significant concerns surrounding the use of hydroxamate derivatives as therapeutic agents in cancer as well as other chronic diseases. Consequently, the research on non-hydroxamate-based HDAC8is is of utmost priority. In the present study, a comprehensive study was presented to unravel the structural requirements of non-hydroxamate-based HDAC8is from a diverse set of 866 compounds. The study utilized Classification-based Quantitative Structure-Activity Relationship (QSAR) analysis, incorporating Bayesian classification, Recursive partitioning, and other machine learning methods to pinpoint the key structural features essential for HDAC8 inhibition. To underscore and gain deeper insights into the identified structural features, molecular docking, and molecular dynamic (MD) simulation studies were conducted. The integration of these computational approaches unveiled key structural motifs essential for potent HDAC8 inhibitory activity, shedding light on the molecular basis of HDAC8 inhibition using non-hydroxamates.
Keywords: Bayesian classification model; HDAC8 inhibitor; Molecular docking; Molecular dynamics simulation; Molecular fingerprints.
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