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Interpretable Attribution Assignment for Octanol-Water Partition Coefficient.

Daisuke Yokogawa1, Kayo Suda1

  • 1Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba Meguro-ku, Tokyo 153-8902, Japan.

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This study introduces a credible graph neural network model for chemical attribution assignment. Our interpretable approach ensures reliable predictions for molecular design and model debugging.

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Area of Science:

  • Chemistry
  • Machine Learning
  • Computational Chemistry

Background:

  • Machine learning model credibility is crucial, especially in chemistry for molecule design and debugging.
  • Current attribution methods focus on atom importance but lack validation of attribution reasonableness.

Purpose of the Study:

  • To develop a credible and interpretable attribution model for chemistry applications.
  • To enhance the reliability of machine learning models in chemical research.

Main Methods:

  • Developed a graph neural network (GNN) model for interpretable attribution in chemistry.
  • Modified the integrated gradients method to assess attribution reasonableness.
  • Predicted the octanol-water partition coefficient (logP) to validate the model.

Main Results:

  • The developed GNN model demonstrated high credibility in attribution assignment.
  • Evaluation metrics including accuracy, consistency, and stability confirmed the approach's reliability.
  • The model successfully predicted logP, showcasing its practical applicability.

Conclusions:

  • The proposed interpretable attribution model enhances the credibility of machine learning in chemistry.
  • This work provides a robust framework for validating attribution in chemical machine learning.
  • The findings support the use of reliable attribution methods for molecular design and model debugging.