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Table 1 The improvement of evaluating parameters calculated for targets from the initial tests

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

ML/Fingerprint 5-HT1A HIV PR Metalloproteinase
   R P MCC R P MCC R P MCC
SMO CDK FP −0.19 0.71 0.54 −0.06 0.80 0.61 −0.18 0.89 0.72
MACCS −0.34 0.20 0.23 −0.18 0.43 0.43 −0.35 0.58 0.47
NB CDK FP −0.07 0.03 0.05 −0.01 0.07 0.07 −0.05 0.05 0.06
MACCS −0.04 0.01 0.04 −0.04 0.03 0.04 −0.06 0.00 0.01
Ibk CDK FP −0.09 0.18 0.30 −0.03 0.42 0.46 −0.09 0.22 0.33
MACCS −0.1 0.08 0.17 −0.06 0.13 0.26 −0.11 0.13 0.25
J48 CDK FP −0.22 0.09 0.16 −0.20 0.11 0.16 −0.17 0.11 0.20
MACCS −0.22 0.07 0.12 −0.16 0.11 0.18 −0.21 0.15 0.22
RF CDK FP −0.34 0.64 0.56 −0.10 0.66 0.60 −0.28 0.74 0.60
MACCS −0.20 0.22 0.31 −0.13 0.30 0.37 −0.19 0.39 0.43
  1. The table shows the changes in given performance parameters for a particular ML method obtained between experiments with the lowest and the highest ratio of negative to positive training examples.