Pretrained Molecular Representations#
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 aself-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.
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 |
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 |
|
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 |
|
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 |
|
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 |