Pretrained Molecular RepresentationsΒΆ

This page contains benchmarks of property prediction models with pre-training.

We have two main methods for the pre-training.

  • Self-supervised pre-training is learning the graph structural information in a
    self-supervised manner. Here we are pre-training on a subset of 2m molecules from
  • Supervised pre-training is doing pre-training on a large supervised dataset.
    Here we are using 456k molecuels and 1,310 tasks from ChEMBL.

For the downstream tasks, we consider the scaffold splitting for molecule data. The split for train/validation/test sets is 80%:10%:10%. For each pre-training method and downstream dataset, we evaluate with 10 random splits and report the mean and the derivation of AUROC metric.

BBBP

Tox21

ToxCast

SIDER

ClinTox

MUV

HIV

BACE

Avg.

No Pretrain

67.1(2.9)

75.0(0.2)

60.6(0.7)

58.9(0.8)

60.8(3.9)

64.3(3.4)

76.4(1.6)

66.5(9.0)

66.2

InfoGraph

68.9(0.6)

76.4(0.4)

71.2(0.6)

59.8(0.7)

70.3(4.2)

69.4(0.8)

75.5(0.7)

73.7(2.6)

70.7

EdgePred

67.1(2.6)

74.6(0.7)

69.8(0.5)

59.4(1.5)

59.0(2.6)

66.8(1.0)

76.3(2.0)

68.4(3.9)

67.7

AttrMasking

65.2(0.9)

75.8(0.5)

70.6(0.6)

58.9(0.9)

79.0(2.3)

68.3(2.1)

76.9(0.9)

78.1(0.8)

71.6

ContextPred

71.1(1.8)

75.6(0.3)

71.1(0.3)

61.7(0.5)

65.9(1.9)

68.5(0.6)

77.1(0.3)

78.6(0.5)

71.2