计算机科学
图形
利用
知识图
理论计算机科学
分子图
药物发现
人工智能
生物信息学
生物
计算机安全
作者
Tsun Ma,Xuan Lin,Bosheng Song,Philip S. Yu,Xiangxiang Zeng
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-12
被引量:8
标识
DOI:10.1109/tkde.2022.3188154
摘要
Molecular interaction prediction is essential in various applications including drug discovery and material science. The problem becomes quite challenging when the interaction is represented by unmapped relationships in molecular networks, namely molecular interaction, because it easily suffers from (i) insufficient labeled data with many false-positive samples, and (ii) ignoring a large number of biological entities with rich information in the knowledge graph. Most of the existing methods cannot properly exploit the information of knowledge graph and molecule graph simultaneously. In this paper, we propose a large-scale Knowledge Graph enhanced Multi-Task Learning model, namely KG-MTL, which extracts the features from both knowledge graph and molecular graph in a synergistic way. Moreover, we design an effective Shared Unit that helps the model to jointly preserve the semantic relations of drug entity and the neighbor structures of the compound in both knowledge graph and molecular graph. Extensive experiments on four real-world datasets demonstrate that our proposed KG-MTL outperforms the state-of-the-art methods on two representative molecular interaction prediction tasks: drug-target interaction prediction and compound-protein interaction prediction. The source code of KG-MTL is available at https://github.com/xzenglab/KG-MTL.
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