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Table 6 The selection of datasets on which models were trained, and details pertaining to these sets

From: Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

Dataset

Tasks

Type

Compounds

Split

Metric

MUV

17

Classification

93,127

Random

PRC-AUC

HIV

1

Classification

41,913

Scaffold

ROC-AUC

BBBP

1

Classification

2053

Scaffold

ROC-AUC

Tox21

12

Classification

8014

Random

ROC-AUC

SIDER

27

Classification

1427

Random

ROC-AUC

QM8

12

Regression

21,786

Random

MAE

ESOL

1

Regression

1128

Random

RMSE

LIPO

1

Regression

4200

Random

RMSE