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Table 1 The statistical performance of SMPLIP-RF models on PDBbind (Release 2015) according to different features compositions

From: SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

Features Train Valid Test
RMSE MAE PCC p_Value RMSE MAE PCC p_Value RMSE MAE PCC p_Value
IFP 0.537 0.420 0.977 0 1.372 1.066 0.724 4.17E-115 1.656 1.349 0.716 1.49E-29
IFP + Int-Dist 0.536 0.422 0.980 0 1.387 1.093 0.720 2.08E-112 1.692 1.388 0.711 5.15E-29
IFP + Frag 0.496 0.381 0.977 0 1.327 1.035 0.747 2.62E-125 1.489 1.227 0.771 8.71E-37
IFP + Int-Dist + Frag 0.494 0.382 0.978 0 1.346 1.054 0.740 8.07E-122 1.512 1.244 0.770 1.43E-36
  1. The Refined set (n = 3481) used for training and validation, and core set (n = 180) as a test data. The boldface represents the model with better statistics from different features combination and max_features options
  2. RMSE root-mean-square-error, MAE mean absolute error, PCC Pearson correlation coefficient, p_value p_value for statistical significance