QSAR analysis of pyrimidine derivatives as VEGFR-2 receptor inhibitors to inhibit cancer using multiple linear regression and artificial neural network - Fariba Masoomi Sefiddashti; Saeid Asadpour; Hedayat Haddadi; Shima Ghanavati Nasab (Wolters Kluwer Medknow Publications, 2021)
Background and purpose: In this study, the pharmacological activity of 33 compounds of furopyrimidine and thienopyrimidine as vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors to inhibit cancer was investigated. The most important angiogenesis inducer is VEGF endothelial growth factor, which exerts its activity by binding to two tyrosine kinase receptors called VEGFR-1 and VEGFR-2. Due to the critical role of VEGF in the pathological angiogenesis of this molecule, it is a valuable therapeutic target for anti-angiogenesis therapies. Experimental approach: After calculating descriptors using SPSS software and stepwise selection method, 5 descriptors were used for modeling in multiple linear regression (MLR) and artificial neural network (ANN). The calibration series and the test series in this study included 26 and 7 combinations, respectively. Findings/Results: The performance evaluation of models was determined by the R2, RMSE, and Q2 statistic parameters. The R2 values of MLR and ANN models were 0.889 and 0.998, respectively. Also, the value of RMSE in the ANN model was lower and its Q2 value was higher than the MLR model. Conclusion and implications: The results were evaluated by different statistical methods and it was concluded that the nonlinear neural network method is powerful to predict the pharmacological activity of similar compounds, and because of the complex and nonlinear relationships, the MLR was not capable of establishing a good model with high predictive power.