Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors.
A linear Quantitative Structure-Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin-2-yl-thiophene-2-hydroxamic acids. In particular, a five-variable model is produced by using the Multiple Linear Regression (MLR) technique and the Elimination Selection-Stepwise Regression Method (ES-SWR) on a database that consists of 58 recently discovered 5-pyridin-2-yl-thiophene-2-hydroxamic acids and 69 descriptors. The physical meaning of the selected descriptors is discussed in detail. The validity of the proposed MLR model is established using the following techniques: cross validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Based on the produced model, an in silico-screening study explores novel structural patterns and suggests new potent lead compounds.
Melagraki
Georgia
G
Department of ChemoInformatics, NovaMechanics Ltd, Larnaca, Cyprus. geomel@mail.ntua.gr
Afantitis
Antreas
A
Sarimveis
Haralambos
H
Koutentis
Panayiotis A
PA
Kollias
George
G
Igglessi-Markopoulou
Olga
O
D000465:Algorithms; D055598:Chemical Phenomena; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D056572:Histone Deacetylase Inhibitors; D006877:Hydroxamic Acids / Q000737:chemistry* / Q000494:pharmacology*; D008956:Models, Chemical; D011725:Pyridines / Q000737:chemistry / Q000494:pharmacology; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D015203:Reproducibility of Results; D013876:Thiophenes / Q000737:chemistry / Q000494:pharmacology
D004791:Enzyme Inhibitors; D056572:Histone Deacetylase Inhibitors; D006877:Hydroxamic Acids; D011725:Pyridines; D013876:Thiophenes