Development of a polynomial scoring function P3-Score for improved scoring and ranking powers

Chuang Li, Aiwei Zhang, Lifei Wang, Jiaqi Zuo, Caizhen Zhu, Jian Xu, Mingliang Wang, John Z.H. Zhang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number140547
JournalChemical Physics Letters
StatePublished - Aug 2023


  • Polynomial Fitting
  • Ridge Regression
  • Scoring Functions

ASJC Scopus subject areas

  • General Physics and Astronomy
  • Physical and Theoretical Chemistry


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