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BridgeDPI: a novel Graph Neural Network for predicting drug-protein interactions
- Source :
- Bioinformatics (Oxford, England). 38(9)
- Publication Year :
- 2021
-
Abstract
- Motivation Exploring drug–protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug–protein association network and predict DPIs by the information of its associated proteins or drugs, called ‘guilt-by-association’ principle. However, the ‘guilt-by-association’ principle is not always true because sometimes similar proteins cannot interact with similar drugs. Recently, learning-based methods learn molecule properties underlying DPIs by utilizing existing databases of characterized interactions but neglect the network-level information. Results We propose a novel method, namely BridgeDPI. We devise a class of virtual nodes to bridge the gap between drugs and proteins and construct a learnable drug–protein association network. The network is optimized based on the supervised signals from the downstream task—the DPI prediction. Through information passing on this drug–protein association network, a Graph Neural Network can capture the network-level information among diverse drugs and proteins. By combining the network-level information and the learning-based method, BridgeDPI achieves significant improvement in three real-world DPI datasets. Moreover, the case study further verifies the effectiveness and reliability of BridgeDPI. Availability and implementation The source code of BridgeDPI can be accessed at https://github.com/SenseTime-Knowledge-Mining/BridgeDPI. The source data used in this study is available on the https://github.com/IBM/InterpretableDTIP (for the BindingDB dataset), https://github.com/masashitsubaki/CPI_prediction (for the C.ELEGANS and HUMAN) datasets, http://dude.docking.org/ (for the DUD-E dataset), repectively.
Details
- ISSN :
- 13674811
- Volume :
- 38
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Bioinformatics (Oxford, England)
- Accession number :
- edsair.doi.dedup.....3ab42f594565fff2739366479a179106