MGACL: Prediction Drug–Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning

The identification of drug–target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network’s false positives due to its bias and negative transfer in the original bipartite graph remains to be clarified. Considering that the impact of heterogeneous auxiliary i...

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Vydané v:	Biomolecules (Basel, Switzerland) Ročník 14; číslo 10; s. 1267
Hlavní autori:	Zhang, Pinglu, Lin, Peng, Li, Dehai, Wang, Wanchun, Qi, Xin, Li, Jing, Xiong, Jianshe
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 01.10.2024 MDPI
Predmet:	Algorithms Area Under Curve Artificial intelligence Associations Comparative analysis Connectivity contrastive learning Customization Disease Drug development Drug discovery Drug Discovery - methods drug–target interaction Genes graph neural networks Graphs heterogeneous graph representation Humans Machine Learning Neural networks Neural Networks, Computer Pharmaceutical Preparations - chemistry Pharmaceutical Preparations - metabolism Proteins - chemistry Proteins - metabolism R&D Research & development Semantics Therapeutic targets Transfer learning China contrastive learning graph neural networks drug–target interaction heterogeneous graph representation
ISSN:	2218-273X, 2218-273X
On-line prístup:	Získať plný text
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