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Table 5 Summary statistics for the five best-performing DNN models

From: Open-source QSAR models for pKa prediction using multiple machine learning approaches

Data option Dataset Feature sets Number of features Train Fivefold CV Test
R2 RMSE Q2 RMSE R2 RMSE
1 Acidic Continuous + MACCS 1408 0.98 0.43 0.75 1.71 0.80 1.51
2 Acidic Continuous + MACCS 1408 0.98 0.52 0.74 1.73 0.79 1.54
2 Acidic Fingerprints 1190 0.98 0.48 0.71 1.82 0.79 1.55
1 Acidic Fingerprints 1190 0.99 0.39 0.71 1.81 0.78 1.59
2 Acidic MACCS 166 0.96 0.64 0.71 1.82 0.77 1.61
2 Basic Fingerprints 1190 0.98 0.48 0.75 1.63 0.77 1.57
1 Basic MACCS 166 0.97 0.53 0.74 1.69 0.77 1.59
1 Basic Continuous + MACCS 1481 0.98 0.45 0.75 1.64 0.76 1.59
2 Basic Continuous + MACCS 1481 0.97 0.56 0.73 1.71 0.76 1.60
2 Basic MACCS 166 0.97 0.58 0.75 1.65 0.74 1.65
1 Combined Continuous + MACCS 1408 0.97 0.52 0.65 1.90 0.75 1.61
1 Combined Fingerprints 1190 0.97 0.55 0.62 1.98 0.73 1.68
2 Combined Continuous + MACCS 1408 0.97 0.55 0.67 1.84 0.72 1.69
1 Combined MACCS 166 0.97 0.57 0.62 1.99 0.72 1.70
2 Combined MACCS 166 0.97 0.52 0.63 1.94 0.70 1.76
  1. Statistics are presented for the acidic only, basic only and combined (acidic and basic) data sets. Each group of statistics is ordered by test set RMSE, with the best-performing models listed first