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TradePool: A Novel Interpretable Framework for Quantifying Atomic Attribution Values in Molecular Property

Bingwei Ni1,2, Wanxiang Shen3, Zhuyifan Ye1

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

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This summary is machine-generated.

We developed a new framework for Graph Neural Network (GNN) interpretability, improving atomic attribution accuracy for faster drug discovery. This enhances AI-driven chemical space exploration and complements expert knowledge.

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Area of Science:

  • Computational chemistry and cheminformatics.
  • Artificial intelligence in drug discovery.
  • Explainable AI (XAI) for molecular modeling.

Background:

  • Graph Neural Networks (GNNs) excel at compound property prediction, especially with limited data.
  • Current GNN interpretability methods struggle with accurate atomic attribution, hindering lead compound optimization.
  • The growing AI-generated chemical space demands efficient and reliable XAI methods.

Purpose of the Study:

  • To propose a novel two-stage framework for calculating atomic attribution values in GNNs.
  • To enhance the accuracy and reliability of GNN interpretability for compound property prediction.
  • To accelerate the drug development process by providing deeper insights into GNN predictions.

Main Methods:

  • A two-stage framework involving model training via structural pooling.
  • Atomic attribution value calculation using substructure mapping.
  • Quantification of task-specific atomic attribution values for GNNs.

Main Results:

  • Achieved 30%/20%/15% enhancement in atomic attribution accuracy for GCNs on aromaticity/LogP/TPSA datasets.
  • Demonstrated high Pearson correlation coefficients (0.93/0.63/0.88), significantly outperforming existing methods (0-0.3).
  • The framework shows robustness to model parameter changes and structural variations.

Conclusions:

  • The proposed interpretable framework significantly improves GNN atomic attribution accuracy.
  • This advancement facilitates more efficient and reliable lead compound optimization in drug discovery.
  • The method offers a valuable tool for accelerating AI-driven research in chemistry and pharmacology.