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Area of Science:

  • Computational Chemistry
  • Artificial Intelligence in Drug Discovery
  • Machine Learning for Cheminformatics

Background:

  • Graph Neural Networks (GNNs) excel at molecular property prediction but lack transparency.
  • Opaque GNN decision-making hinders adoption in drug discovery.
  • Activity Cliffs (ACs) present challenges due to structural similarity and potency variations.

Purpose of the Study:

  • To introduce the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework.
  • To improve both predictive accuracy and interpretability of GNNs in molecular modeling.
  • To provide chemist-friendly explanations for GNN predictions, particularly for ACs.

Main Methods:

  • Developed the ACES-GNN framework integrating explanation supervision for ACs.
  • Trained GNNs with a focus on aligning model attributions with chemical interpretations.
  • Validated the framework across 30 pharmacological targets.

Main Results:

  • ACES-GNN consistently improved predictive accuracy compared to unsupervised GNNs.
  • Enhanced attribution quality for identifying and explaining ACs.
  • Demonstrated a positive correlation between improved predictions and accurate explanations.

Conclusions:

  • ACES-GNN offers a robust and adaptable framework for understanding and interpreting ACs.
  • Explanation-guided learning advances interpretable AI in molecular modeling.
  • The framework bridges the gap between GNN prediction and chemical understanding in drug discovery.