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• W4313415050 abstract "Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug. However, existing deep-learning DTA prediction methods do not consider the interactions between drug substructures and protein sub-sequences. This work proposes GraphATT-DTA, a DTA prediction model that constructs the essential regions for determining interaction affinity between compounds and proteins, modeled with an attention mechanism for interpretability. We make the model consider the local-to-global interactions with the attention mechanism between compound and protein. As a result, GraphATT-DTA shows an improved prediction of DTA performance and interpretability compared with state-of-the-art models. The model is trained and evaluated with the Davis dataset, the human kinase dataset; an external evaluation is achieved with the independently proposed human kinase dataset from the BindingDB dataset." @default.
• W4313415050 created "2023-01-06" @default.
• W4313415050 creator A5030104461 @default.
• W4313415050 creator A5043581759 @default.
• W4313415050 date "2022-12-27" @default.
• W4313415050 modified "2023-10-18" @default.
• W4313415050 title "GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity" @default.
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• W4313415050 doi "https://doi.org/10.3390/biomedicines11010067" @default.
• W4313415050 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36672575" @default.
• W4313415050 hasPublicationYear "2022" @default.
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