Table 12 Comparison of drug-target interaction prediction models with state-of-arts (SOTA) works.

From: Predicting drug-target interactions using machine learning with improved data balancing and feature engineering

SI. No.

Author

Dataset

Model

Sensitivity

Specificity

AUC-ROC

MSE

RMSE

1

Wei et al.9

BindingDB-Kd

DeepLPI

68.40

77.30

79.00

–

–

2

Schuh et al.12

BindingDB-Kd

BarlowDTI

–

–

93.64

–

–

3

Guichaoua et al.13

BindingDB-Kd

Komet

–

–

70.00

–

–

4

Our Proposed

BindingDB-Kd

GAN+RFC

97.46

98.82

99.42

2.54

15.95

5

Pei et al.10

BindingDB-Ki

Ada-kNN-DTA

–

–

–

–

73.50

6

Zhu et al.11

BindingDB-Ki

MDCT-DTA

–

-

–

47.50

–

7

Our Proposed

BindingDB-Ki

GAN+RFC

91.69

93.40

97.32

8.31

28.82

8

Pei et al.10

BindingDB-IC50

Ada-kNN-DTA

–

–

–

–

67.50

9

Pei et al.14

BindingDB-IC50

Ada-kNN-DTA

–

–

–

–

73.50

10

Our Proposed

BindingDB-IC50

GAN+RFC

95.40

95.42

98.97

4.60

21.46