Name
Abstract | ||
|---|---|---|
In this work, we proposed a self-supervised learning method, ATMOL, for molecular representation learning and properties prediction. We developed a novel molecular graph augmentation strategy, referred to as attention-wise graph masking, to generate challenging positive samples for contrastive learning. We adopted the graph attention network as the molecular graph encoder, and leveraged the learned attention weights as masking guidance to generate molecular augmentation graphs. By minimization of the contrastive loss between original graph and augmented graph, our model can capture important molecular structure and higher order semantic information. Extensive experiments showed that our attention-wise graph mask contrastive learning exhibited state-of-the-art performance in a couple of downstream molecular property prediction tasks. We also verified that our model pretrained on larger scale of unlabeled data improved the generalization of learned molecular representation. Moreover, visualization of the attention heatmaps showed meaningful patterns indicative of atoms and atomic groups important to specific molecular property. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/BIB/BBAC303 | Briefings in Bioinformatics |
Keywords | DocType | Volume |
Attention mechanism,Contrastive learning,Graph attention network,Graph augmentation,Molecular property | Journal | 23 |
Issue | ISSN | Citations |
5 | 1477-4054 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. H. Liu | 1 | 40 | 16.40 |
| Yibiao Huang | 2 | 0 | 0.34 |
| Xuejun Liu | 3 | 0 | 1.35 |
| Lei Deng | 4 | 63 | 15.72 |