Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction

• URL: http://arxiv.org/abs/2208.11267v2
• Date: Thu, 25 Aug 2022 05:27:09 GMT
• Title: Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction
• Authors: Xinyu Zhu, Yongliang Shen, Weiming Lu
• Abstract summary: Concomitant administration of drugs can cause drug-drug interactions (DDIs) We propose a novel model, Molecular Substructure-Aware Network (MSAN), to effectively predict potential DDIs from molecular structures of drug pairs.
• Score: 10.157966744159491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Concomitant administration of drugs can cause drug-drug interactions (DDIs). Some drug combinations are beneficial, but other ones may cause negative effects which are previously unrecorded. Previous works on DDI prediction usually rely on hand-engineered domain knowledge, which is laborious to obtain. In this work, we propose a novel model, Molecular Substructure-Aware Network (MSAN), to effectively predict potential DDIs from molecular structures of drug pairs. We adopt a Transformer-like substructure extraction module to acquire a fixed number of representative vectors that are associated with various substructure patterns of the drug molecule. Then, interaction strength between the two drugs' substructures will be captured by a similarity-based interaction module. We also perform a substructure dropping augmentation before graph encoding to alleviate overfitting. Experimental results from a real-world dataset reveal that our proposed model achieves the state-of-the-art performance. We also show that the predictions of our model are highly interpretable through a case study.

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