SAR study on inhibitors of Hsp90α using machine learning methods

Heat shock protein 90 (Hsp90) is a promising target for cancer treatment, developing new effective Hsp90 inhibitors is of great significance in anticancer therapy. In this study, 20 machine learning models were constructed on 1321 molecules in order to precisely classify highly active and weakly active Hsp90 inhibitors. Six types of fingerprints including MACCS keys (MACCS), Extended connectivity fingerprints with radius 2 (ECFP_4), PubChem fingerprints, Estate fingerprints, Substructure fingerprints and 2D atom pairs fingerprints were applied to characterize Hsp90 inhibitors. Five machine learning algorithms containing support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT) and multilayer perceptron (MLP) were utilized to develop classification models. The best RF and SVM models resulted in MCC values of 0.8070 and 0.8003, respectively. The fingerprints of these best models were analyzed by information gain (IG) method. Based on the IG analysis, we found some favorable substructures of highly active Hsp90 inhibitors. Moreover, we clustered 1321 Hsp90 inhibitors into eight subsets, further analyzed and summarized the structural characteristics of each subset. It was found that purine scaffold and resorcinol appeared frequently in highly active Hsp90 inhibitors.