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.