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Rethinking the Masking Strategy for Pretraining Molecular Graphs from a Data-Centric View.
Wei Lin1, Chi Chung Alan Fung1
1Department of Neuroscience, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon 999077, Hong Kong, China.
ACS Omega
|May 20, 2024
Summary
A new weighted masking strategy improves molecular graph pretraining by addressing imbalanced atom distribution. This method enhances model capacity and performance without adding parameters, outperforming random masking.
Area of Science:
- Computational chemistry
- Machine learning
- Drug discovery
Background:
- Node-level self-supervised learning is crucial for pretraining molecular graphs.
- Attribute Masking (AttrMask) is a key method, with enhancements focusing on model architecture.
- Existing methods neglect imbalanced atom distribution from random masking.
Purpose of the Study:
- To introduce a weighted masking strategy for more effective molecular graph pretraining.
- To enhance the capacity of pretrained models by better utilizing molecular information.
- To improve performance over random masking without increasing model parameters.
Main Methods:
- Proposed a novel weighted masking strategy tailored to molecular properties.
- Applied the strategy to the Attribute Masking (AttrMask) framework.
- Evaluated performance against random masking and model-centric approaches.
Main Results:
- The weighted masking strategy significantly improved performance compared to random masking.
- Performance gains were achieved without increasing model parameters.
- The strategy demonstrated effectiveness and potential for broader application.
Conclusions:
- Weighted masking is a superior alternative to random masking for molecular graph pretraining.
- This approach enhances model capacity and performance by addressing atom distribution.
- The proposed strategy is adaptable to other pretraining methods for improved results.
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