Drug-target interaction prediction by integrating heterogeneous information with mutual attention network
Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these...
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Zusammenfassung: | Identification of drug-target interactions is an indispensable part of drug
discovery. While conventional shallow machine learning and recent deep learning
methods based on chemogenomic properties of drugs and target proteins have
pushed this prediction performance improvement to a new level, these methods
are still difficult to adapt to novel structures. Alternatively, large-scale
biological and pharmacological data provide new ways to accelerate drug-target
interaction prediction. Here, we propose DrugMAN, a deep learning model for
predicting drug-target interaction by integrating multiplex heterogeneous
functional networks with a mutual attention network (MAN). DrugMAN uses a graph
attention network-based integration algorithm to learn network-specific
low-dimensional features for drugs and target proteins by integrating four drug
networks and seven gene/protein networks, respectively. DrugMAN then captures
interaction information between drug and target representations by a mutual
attention network to improve drug-target prediction. DrugMAN achieves the best
prediction performance under four different scenarios, especially in real-world
scenarios. DrugMAN spotlights heterogeneous information to mine drug-target
interactions and can be a powerful tool for drug discovery and drug
repurposing. |
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DOI: | 10.48550/arxiv.2404.03516 |