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Table 9 The top three model and corresponding performances based on the results from 50 times independent runs for each dataset. (the descriptor-based models were colored as italic and the graph-based model were colored as undeline)

From: Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

Dataset

No.

Tasks

Metric

Top 1

Top 2

Top 3

ESOL

1127

1

RMSE

SVM (0.569 ± 0.052)

XGBoost (0.582 ± 0.056)

Attentive FP (0.587 ± 0.065)

FreeSolv

639

1

RMSE

SVM (0.852 ± 0.171)

DNN (1.013 ± 0.197)

XGBoost (1.025 ± 0.185)

Lipop

4200

1

RMSE

Attentive FP (0.553 ± 0.035)

XGBoost (0.574 ± 0.034)

SVM (0.577 ± 0.039)

HIV

40748

1

AUC_ROC

GCN (0.834 ± 0.025)

GAT (0.826 ± 0.030)

SVM (0.822 ± 0.020)

BACE

1513

1

AUC_ROC

GCN (0.898 ± 0.019)

SVM (0.893 ± 0.020)

RF (0.890 ± 0.022)

BBBP

2035

1

AUC_ROC

RF (0.927 ± 0.025)

XGBoost (0.926 ± 0.026)

DNN (0.922 ± 0.029)

ClinTox

1475

2

AUC_ROC

XGBoost (0.911 ± 0.036)

RF (0.911 ± 0.042)

Attentive FP (0.904 ± 0.043)

SIDER

1366

27

AUC_ROC

RF (0.646 ± 0.022)

XGBoost (0.642 ± 0.020)

GCN (0.634 ± 0.026)

Tox21

7811

12

AUC_ROC

Attentive FP (0.852 ± 0.012)

DNN (0.840 ± 0.014)

RF (0.838 ± 0.011)

ToxCast

8539

182

AUC_ROC

Attentive FP (0.794 ± 0.017)

DNN (0.786 ± 0.019)

RF (0.782 ± 0.005)

MUV

93087

17

AUC_PRC

SVM (0.112 ± 0.045)

XGBoost (0.068 ± 0.028)

RF (0.061 ± 0.032)