Abstract
The continuous optimization of novel EGFR inhibitors is a hot topic of cancer drug research. In this study, a total of twelve EGFR consensus scoring models were trained. The results showed that the best scoring model 3 had acceptable statistical parameters: for the training set, R2 = 0.648, RMSEtrain = 0.018, Q2 = 0.543, RMSELOO = 0.021, and for the test set, R2 = 0.678, RMSEtest = 0.013. Compared to any single scoring function, model 3 exhibited potent scoring power for various types of EGFR inhibitors and can be used in a large-scale virtual screening of EGFR inhibitors.
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