Skip to main content

Table 8 External performance of continuous 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

 

SEN

SPE

MCC

BA

#AD

%AD

CS

nT

0.794

0.796

0.587

0.795

2665

0.924

1

0.840

0.841

0.677

0.840

2182

0.757

2

0.878

0.858

0.733

0.868

1704

0.591

3

0.913

0.883

0.794

0.898

1222

0.424

4

vT

0.743

0.938

0.577

0.840

2742

0.949

1

0.796

0.976

0.737

0.886

2316

0.802

2

0.890

0.978

0.820

0.934

1556

0.539

3

EPA

0.602

0.856

0.439

0.729

2653

0.928

1

0.701

0.885

0.550

0.793

1731

0.605

2

0.739

0.898

0.600

0.819

1200

0.420

3

GHS

0.567

0.894

0.461

0.731

1561

0.542

1

0.644

0.911

0.541

0.777

908

0.315

2

0.676

0.916

0.573

0.796

617

0.214

3

  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, with respect to the CS threshold for defining predictions in AD. For multi-category endpoints (EPA and GHS), SEN and SPE are the average of sensitivities/specificities computed separately for each class, while BA is the arithmetic mean of the average SEN and SPE
Back to article page

Journal of Cheminformatics

ISSN: 1758-2946

Contact us