Display omitted •A polynomial scoring function P3-Score gives better scoring power (0.735) and ranking power (0.688) on CASF-2016 test set.•The multivariate polynomial ridge regression is a promising method to improve the traditional scoring function performance.•The constructed 14 feature terms can be used to develop a new scoring function. Scoring functions are of great importance in fast evaluations of the protein–ligand binding affinity. To improve the scoring power and ranking power, some new features are constructed, and a new empirical scoring function (P3-Score) using 14 features was developed based on multivariate polynomial ridge regression and k-fold cross-validation on the training set. The scoring power and ranking power of P3-Score are compared with other 36 classical scoring functions on the test set in CASF-2016, the results indicate that P3-Score gives better scoring power (0.735) and ranking power (0.688) than the current empirical scoring functions. The multivariate polynomial ridge regression could be a promising method to improve the classical scoring function and prevent overfitting. However, in comparison with recently developed machine learning scoring functions, most ML scoring functions present better scoring performance than the classical scoring functions.