Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling
Keywords: drug discovery, graph neural networks
TL;DR: A new interpretable and chirality-aware graph neural network for drug discovery.
Abstract: In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular Kernel Graph Neural Network (MolKGNN) for molecular representation learning, which features conformation invariance, chirality-awareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming MolKGNN's pragmatic interpretability.
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Type Of Submission: Extended abstract (max 4 main pages).
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Type Of Submission: Extended abstract.
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