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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Vydáno v:	Biomolecules (Basel, Switzerland) Ročník 14; číslo 10; s. 1267
Hlavní autoři:	Zhang, Pinglu, Lin, Peng, Li, Dehai, Wang, Wanchun, Qi, Xin, Li, Jing, Xiong, Jianshe
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 01.10.2024 MDPI
Témata:	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 přístup:	Získat plný text
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Popis
Shrnutí:	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 information on DTI varies depending on the drug and target, we established an adaptive enhanced personalized meta-knowledge transfer network named Meta Graph Association-Aware Contrastive Learning (MGACL), which can transfer personalized heterogeneous auxiliary information from different nodes and reduce data bias. Meanwhile, we propose a novel DTI association-aware contrastive learning strategy that aligns high-frequency drug representations with learned auxiliary graph representations to prevent negative transfer. Our study improves the DTI prediction performance by about 3%, evaluated by analyzing the area under the curve (AUC) and area under the precision–recall curve (AUPRC) compared with existing methods, which is more conducive to accurately identifying drug targets for the development of new drugs.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work.
ISSN:	2218-273X 2218-273X
DOI:	10.3390/biom14101267