Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbac140
Keywords
drug-drug interactions; graph neural network; knowledge graph; attention-based representation learning
Funding
- Natural Science Foundation of Xinjiang Uygur Autonomous Region [2021D01D05]
- Pioneer Hundred Talents Program of Chinese Academy of Sciences
- National Natural Science Foundation of China [62172355]
- NSFC [61722212]
- Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project [2018AAA0100100]
- Tianshan youth - Excellent Youth [2019Q029]
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This paper proposes a KG-based drug-drug interaction (DDI) prediction framework called DDKG, which utilizes KG information and attention mechanism. Experimental results show that DDKG outperforms existing algorithms on different evaluation metrics.
Drug-drug interactions (DDIs) are known as the main cause of life-threatening adverse events, and their identification is a key task in drug development. Existing computational algorithms mainly solve this problem by using advanced representation learning techniques. Though effective, few of them are capable of performing their tasks on biomedical knowledge graphs (KGs) that provide more detailed information about drug attributes and drug-related triple facts. In this work, an attention-based KG representation learning framework, namely DDKG, is proposed to fully utilize the information of KGs for improved performance of DDI prediction. In particular, DDKG first initializes the representations of drugs with their embeddings derived from drug attributes with an encoder-decoder layer, and then learns the representations of drugs by recursively propagating and aggregating first-order neighboring information along top-ranked network paths determined by neighboring node embeddings and triple facts. Last, DDKG estimates the probability of being interacting for pairwise drugs with their representations in an end-to-end manner. To evaluate the effectiveness of DDKG, extensive experiments have been conducted on two practical datasets with different sizes, and the results demonstrate that DDKG is superior to state-of-the-art algorithms on the DDI prediction task in terms of different evaluation metrics across all datasets.
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