Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries.
Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.
a Hanoi University of Pharmacy , Hanoi , Vietnam.
H
H
Pham-The
b Department of Systems and Computer Engineering , Carleton University , Ottawa , ON , Canada.
G
G
Casañola-Martin
c Faculty of Life Sciences , Amazonian State University , Puyo , Pastaza , Ecuador.
K
K
Diéguez-Santana
a Hanoi University of Pharmacy , Hanoi , Vietnam.
N
N
Nguyen-Hai
a Hanoi University of Pharmacy , Hanoi , Vietnam.
N T
NT
Ngoc
d School of Medicine and Pharmacy, Vietnam National University , Hanoi , Vietnam.
L
L
Vu-Duc
d School of Medicine and Pharmacy, Vietnam National University , Hanoi , Vietnam.
H
H
Le-Thi-Thu
D055808:Drug Discovery / Q000379:methods; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D056572:Histone Deacetylase Inhibitors / Q000737:chemistry* / Q000378:metabolism*; D020128:Inhibitory Concentration 50; D000069550:Machine Learning; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D054852:Small Molecule Libraries
D056572:Histone Deacetylase Inhibitors; D054852:Small Molecule Libraries
Histone deacetylase inhibitor; chemoinformatics; machine learning; quantitative structure–activity relationship; virtual screening