Pretraining graph neural networks with ELECTRA

Wengong Jin

Molecular property prediction is an important task in cheminformatics. Current property prediction methods are based on graph neural networks and require a large amount of training data to achieve state-of-the-art performance. Unfortunately, most datasets in cheminformatic domains are small (e.g., less than 1000). On the other hand, pretraining methods have achieved great success in computer vision and natural language processing. In this project, we seek to investigate how to pretrain graph neural networks on a large collection of unlabeled molecules using ELECTRA (Clark et al., 2020). The goal is to learn a masked language model to generate corrupted molecules and train a discriminator to distinguish the real molecules from the fake molecules. The method will be evaluated on MoleculeNet benchmark (Wu et al., 2017) to test its empirical performance.


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Pretraining graph neural networks with ELECTRA

Abstract