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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 R 2 test‑FGFR4 = 0.80 and R 2 test‑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.

The details of the synthesis process and structure spectra data for the target compounds as well as the intermediates.Ethyl 2-methyl-4-nitrophenylacetate 1 2.80g (50.0mmol) of 2-methyl-4-nitrophenylacetonitrile, 100mL of absolute ethanol and 490mg (5.00mmol) of concentrated H 2 SO 4 were added to a 250mL pressure bottle. The reaction mixture was reacted at 120 °C for 24h, cooled. The reaction solution was concentrated and extracted with ethyl acetate(100 mL×2). The combined organic phase was washed with water and dried over anhydrous magnesium sulfate, and then purified by column chromatography to obtain 9.64 g of colorless oil, the yield was 86.4%. MS m/z: 246.0[M+Na + , 222.1[M-H] -; 1 H-NMR(CDCl 3 , 300MHz) δ(ppm): 8.05(m, 2H, ArH), 7.38(d, 1H, ArH), 4.19(q, 2H, CH 2 CH 3 ), 3.73(s, 2H, CH 2 COO), 2.43(s, 3H), 1.27(t, 3H, CH 2 CH 3 ). 2-(2-methyl-4-nitrophenyl)ethanol 2 8.92 g (40.0 mmol) of ethyl 2-methyl-4-nitrophenylacetate 1, 100 mL of absolute ethanol and (6.13 g, 160 mmol) of NaBH 4 were placed in a 250 mL reaction flask. The reaction mixture was reacted at room temperature overnight. The reaction solution was evaporated under reduced pressure and purified by column chromatography to afford 6.75g. The yield was 93.2%. MS m/z: 182.1[M+H] + . 2-(2-bromoethyl) 4-nitrotoluene 3 6.34 g (35.0 mmol) of 2-(2-methyl-4-nitrophenyl) ethanol 2, 50 mL of anhydrous DCM, 12.2 g (36.8 mmol) of carbon tetrabromide and 9.65 g (36.8 mmol) of triphenylphosphine were added to a 250 mL reaction flask. The reaction mixture was

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

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