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Table 1 Test set performances of random forest models trained on data generated by Nonpher, SAscore and DR approaches

From: Nonpher: computational method for design of hard-to-synthesize structures

Model Acc (%) SN (%) SP (%) AUC
Nonpher 89.6 93.8 77.0 0.94
SAscore 82.5 94.7 46.0 0.89
DR 46.0 30.8 91.5 0.60
  1. An accuracy (Acc), sensitivity (SN), specificity (SP) and an area under a ROC curve (AUC) are calculated as average values from five different random samples of the \( S_{train} \) data set