Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation

Kinase inhibitors are widely used in antitumor research, but there are still many problems such as drug resistance and off-target toxicity. A more suitable solution is to design a multitarget inhibitor with certain selectivity. Herein, computational and experimental studies were applied to the discovery of dual inhibitors against FGFR4 and EGFR. A quantitative structure–property relationship (QSPR) study was carried out to predict the FGFR4 and EGFR activity of a data set consisting of 843 and 5088 compounds, respectively. Four different machine learning methods including support vector machine (SVM), random forest (RF), gradient boost regression tree (GBRT), and XGBoost (XGB) were built using the most suitable features selected by the mutual information algorithm. As for FGFR4 and EGFR, SVM showed the best performance with R2test-FGFR4 = 0.80 and R2test-EGFR = 0.75, demonstrating excellent model stability, which was used to predict the activity of some compounds from an in-house database. Finally, compound 1 was selected, which exhibits inhibitory activity against FGFR4 (IC50 = 86.2 nM) and EGFR (IC50 = 83.9 nM) kinase, respectively. Furthermore, molecular docking and molecular dynamics simulations were performed to identify key amino acids for the interaction of compound 1 with FGFR4 and EGFR. In this paper, the machine-learning-based QSAR models were established and effectively applied to the discovery of dual-target inhibitors against FGFR4 and EGFR, demonstrating the great potential of machine learning strategies in dual inhibitor discovery.