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Table 5 The prediction performance on benchmark datasets and statistical comparison of SMPLIP-Score with reported models

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

Datasets Models SETS PCC RMSE MAE p_Value Sp Refs
Astex Diverse Set SMPLIP-Score 0.724 1.177 0.938 0.002 0.764
CSAR NRC HiQ Set01 0.785 1.903 1.500 8.03E−13 0.761
Set02 0.803 1.475 1.134 1.54E−15 0.823
FEP BACE 0.239 0.639 0.505 0.160 0.250
MCL1 0.077 1.045 0.797 0.629 0.146
PTP1B 0.634 0.768 0.536 0.002 0.680
Thrombin − 0.645 0.962 0.780 0.321 − 0.536
Tyk2 0.469 0.859 0.655 0.078 0.546
PDBbind NMR 0.209 1.857 1.552 0.004 0.234
Astex Diverse Set DeepAtom 0.768 1.027 0.714 Li et al. [25]
RF-Score 0.710 1.144 0.891 Ballester et al. [63]
Pafnucy 0.569 1.374 1.110 Stepniewska Dziubinska et al. [28]
Res4HTMD 1.54 0.07 0.41 Hassan Harrirou et al. [29]
RosENet 1.84 0.21 0.29
CSAR NRC HiQ KDEPP Set01 0.72 2.09 Jiménez et al. [24]
Set02 0.65 1.92
Res4HTMD Set01 1.75 2E−15 0.84 Hassan Harrirou et al. [29]
Set02 1.34 3E−13 0.83
RosENet Set01 1.71 2E−17 0.87
Set02 1.38 2E−14 0.85
FEP KDEEP BACE − 0.06 0.84 Jiménez et al. [24]
MCL1 0.34 1.04
PTP1B 0.58 0.93
Thrombin 0.58 0.44
Tyk2 − 0.22 1.13
Res4HTMD BACE 1.27 0.26 − 0.19 Hassan Harrirou et al. [29]
MCL1 1.1 2E−3 0.45
PTP1B 0.88 6E−3 0.55
Thrombin 0.83 0.16 0.45
Tyk2 0.76 2E−3 0.71
PDBbind NMR RosENet 1.37 0.56
  1. Models ML/DL method used to build the ligand binding affinity prediction model, RMSE root-mean-square-error, MAE mean absolute error, PCC Pearson correlation coefficient, p_value p_value for statistical significance, Sp Spearman correlation coefficient, RF-Model RF parameters includes: n_estimators = 500; max_features = “AUTO”