Next, the scoring power was assessed to evaluate the model's ability to predict and reproduce experimental binding constants using linear and multiple linear regression. The correlation between experimental binding affinities and scores of the best-poses from different scoring functions was analyzed through linear regression, and the R 2 values were calculated to assess the quality of the fitting. For DBX2, graph-level predictions were utilized to evaluate the correlation with experimental binding affinities. Additionally, multiple linear regression was conducted to correlate experimental binding affinities with predicted values derived from various linear combinations of scoring functions, as described in a previous study .