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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