In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (J.Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-Learning parametrization, a new scoring function ΔLinF9XGB is developed. The scoring power of ΔLinF9XGB for locally optimized poses, flexibile re-docked poses and ensemble docked poses of CASF-2016 core set achieve Pearson's correlation coefficient (R) of 0.853, 0.839 and 0.813, respectively.
Chao Yang and Yingkai Zhang,
J. Chem. Inf. Model.,
2022, 62, ,
Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein–Ligand Scoring Functions