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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.

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Summary
This summary is machine-generated.

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.

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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.