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Table 6 External performance of single models for predicting classification endpoints (vT, nT, EPA, GHS)

From: SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

  Model SEN SPE MCC BA #AD %AD
nT BRF 0.830 0.848 0.674 0.839 2100 0.728
aiQSAR 0.723 0.829 0.556 0.776 2567 0.890
SARpy 0.772 0.724 0.492 0.748 2488 0.863
GLM 0.779 0.650 0.425 0.714 2884 1.000
vT BRF 0.856 0.903 0.585 0.880 2103 0.728
aiQSAR 0.682 0.963 0.619 0.822 2572 0.891
SARpy 0.710 0.896 0.467 0.803 2613 0.905
EPA BRF 0.614 0.851 0.405 0.733 2301 0.805
aiQSAR 0.603 0.857 0.450 0.730 2547 0.891
HPT-RF 0.616 0.860 0.462 0.738 2180 0.763
GHS BRF 0.539 0.872 0.342 0.705 1410 0.490
aiQSAR 0.568 0.895 0.469 0.731 1475 0.512
HPT-RF 0.569 0.897 0.476 0.733 1291 0.448
  1. For each model, the sensitivity (SEN), the specificity (SPE), the balanced accuracy (BA), the Matthew’s correlation coefficient (MCC), the number (#AD) and the percentage (%AD) of predictions in AD are reported. For multi-category endpoints (EPA and GHS), SEN and SPE are the average of values computed separately for each class, while BA is the arithmetic mean of the average SEN and SPE. The best values for each metric are italicized