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Table 2 The improvement of MCC values calculated for targets from the confirmatory tests

From: The influence of negative training set size on machine learning-based virtual screening

Target ChEMBL class Train/test CDK FP/MCC
    SMO NB Ibk J48 RF
D2 membrane receptor 310/1407 0.55 0.04 0.48 0.25 0.65
EGFR enzyme/kinase 280/1303 0.35 0.09 0.50 0.41 0.61
Mu opioid unclassified protein 270/1235 0.53 0.05 0.55 0.33 0.65
SERT transporter 390/1822 0.25 0.03 0.62 0.40 0.55
Estrogen α transcription factor 133/614 0.47 0.10 0.46 0.28 0.67
AChE enzyme/hydrolase 162/743 0.58 0.10 0.41 0.23 0.69
Factor Xa enzyme/protease 530/2439 0.54 0.02 0.58 0.39 0.67
Thrombin enzyme/protease 370/1691 0.59 0.04 0.58 0.27 0.66
PDE5 enzyme/phosphodiesterase 152/695 0.56 0.01 0.36 0.28 0.60
Renin enzyme/protease 340/1556 0.46 0.06 0.59 0.33 0.65
Glucocorticoid transcription factor 236/1084 0.62 0.03 0.56 0.31 0.74
CRF1 membrane receptor 200/914 0.59 0.03 0.46 0.34 0.74
  1. The table shows the changes in MCC for a particular ML method obtained between experiments with the lowest and the highest ratio of negative to positive training examples.