title: A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors.
abstract: Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure-activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery.
authors.affiliation: a Department of Biochemistry, Division of Applied Life Science (BK21 Plus Program) , Systems and Synthetic Agrobiotech Centre (SSAC), Plant Molecular Biology and Biotechnology Research Centre (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University , Jinju , Republic of Korea.
authors.forename: G P
authors.initials: GP
authors.lastname: Cao
authors.forename: M
authors.initials: M
authors.lastname: Arooj
authors.forename: S
authors.initials: S
authors.lastname: Thangapandian
authors.forename: C
authors.initials: C
authors.lastname: Park
authors.forename: V
authors.initials: V
authors.lastname: Arulalapperumal
authors.forename: Y
authors.initials: Y
authors.lastname: Kim
authors.forename: Y J
authors.initials: YJ
authors.lastname: Kwon
authors.forename: H H
authors.initials: HH
authors.lastname: Kim
authors.forename: J K
authors.initials: JK
authors.lastname: Suh
authors.forename: K W
authors.initials: KW
authors.lastname: Lee
mesh_terms: D062126:Databases, Chemical; D055808:Drug Discovery; D056572:Histone Deacetylase Inhibitors / Q000737:chemistry*; D006655:Histone Deacetylases / Q000737:chemistry*; D000069550:Machine Learning; D008956:Models, Chemical; D056004:Molecular Dynamics Simulation; D025341:Principal Component Analysis; D021281:Quantitative Structure-Activity Relationship
chemical_list: D056572:Histone Deacetylase Inhibitors; D006655:Histone Deacetylases
keywords: K nearest neighbours; MD simulation; histone deacetylases 8; molecular docking; neighbourhood classifier; virtual screening