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Table 1 AUC performance in the cross-validation experiment on the ChEMBL dataset

From: Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Cutoff (\(\upmu \hbox {M}\)) MaxSim MeanSim 11NN IRV PS-IRV SVM RF
All datasets
 \(1\) 0.79 0.76 0.81 0.86 0.86 0.84 0.84
  \(5\) 0.76 0.74 0.82 0.84 0.85 0.84 0.82
  \(10\) 0.75 0.73 0.81 0.84 0.85 0.84 0.82
Datasets with fewer than 100 molecules
 \(1\) 0.75 0.75 0.75 0.78 0.78 0.77 0.78
 \(5\) 0.72 0.73 0.74 0.74 0.76 0.77 0.76
 \(10\) 0.71 0.71 0.74 0.75 0.75 0.76 0.76
Datasets with more than 100 molecules
 \(1\) 0.80 0.76 0.73 0.87 0.88 0.85 0.85
 \(5\) 0.77 0.74 0.84 0.87 0.88 0.86 0.84
 \(10\) 0.77 0.73 0.84 0.87 0.88 0.86 0.86
Datasets with more than 200 molecules
 \(1\) 0.81 0.75 0.84 0.89 0.89 0.86 0.86
 \(5\) 0.78 0.74 0.86 0.89 0.90 0.87 0.86
 \(10\) 0.77 0.73 0.86 0.88 0.90 0.87 0.85
  1. Each section of the table shows the average performance for datasets of different sizes
  2. Best results within each group are in italics