Matches in SemOpenAlex for { <https://semopenalex.org/work/W4378782647> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4378782647 abstract "Large-scale prediction of drug-target affinity (DTA) plays an important role in drug discovery. In recent years, machine learning algorithms have made great progress in DTA prediction by utilizing sequence or structural information of both drugs and proteins. However, sequence-based algorithms ignore the structural information of molecules and proteins, while graph-based algorithms are insufficient in feature extraction and information interaction.In this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for interpretable DTA prediction. It can adaptively acquire feature representations of drugs and proteins and allow information to interact at the graph level, effectively combining the advantages of both sequence-based and graph-based approaches. Experimental results have shown that NHGNN-DTA achieved new state-of-the-art performance. It achieved the mean squared error (MSE) of 0.196 on the Davis dataset (below 0.2 for the first time) and 0.124 on the KIBA dataset (3% improvement). Meanwhile, in the case of cold start scenario, NHGNN-DTA proved to be more robust and more effective with unseen inputs than baseline methods. Furthermore, the multi-head self-attention mechanism endows the model with interpretability, providing new exploratory insights for drug discovery. The case study on Omicron variants of SARS-CoV-2 illustrates the efficient utilization of drug repurposing in COVID-19.The source code and data are available at https://github.com/hehh77/NHGNN-DTA." @default.
- W4378782647 created "2023-06-01" @default.
- W4378782647 creator A5022225728 @default.
- W4378782647 creator A5038065195 @default.
- W4378782647 creator A5078562310 @default.
- W4378782647 date "2023-05-30" @default.
- W4378782647 modified "2023-10-05" @default.
- W4378782647 title "NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug–target binding affinity prediction" @default.
- W4378782647 cites W1975147762 @default.
- W4378782647 cites W2035585923 @default.
- W4378782647 cites W2044424923 @default.
- W4378782647 cites W2086286404 @default.
- W4378782647 cites W2544136853 @default.
- W4378782647 cites W2767891136 @default.
- W4378782647 cites W2775714759 @default.
- W4378782647 cites W2785947426 @default.
- W4378782647 cites W2806547269 @default.
- W4378782647 cites W2906084236 @default.
- W4378782647 cites W3005769002 @default.
- W4378782647 cites W3096561213 @default.
- W4378782647 cites W3175786839 @default.
- W4378782647 cites W3177828909 @default.
- W4378782647 cites W3186179742 @default.
- W4378782647 cites W3189831819 @default.
- W4378782647 cites W3212854871 @default.
- W4378782647 cites W3215525389 @default.
- W4378782647 cites W4200547951 @default.
- W4378782647 cites W4205167309 @default.
- W4378782647 cites W4220757799 @default.
- W4378782647 cites W4296551289 @default.
- W4378782647 cites W4297179162 @default.
- W4378782647 cites W4319915126 @default.
- W4378782647 doi "https://doi.org/10.1093/bioinformatics/btad355" @default.
- W4378782647 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37252835" @default.
- W4378782647 hasPublicationYear "2023" @default.
- W4378782647 type Work @default.
- W4378782647 citedByCount "2" @default.
- W4378782647 countsByYear W43787826472023 @default.
- W4378782647 crossrefType "journal-article" @default.
- W4378782647 hasAuthorship W4378782647A5022225728 @default.
- W4378782647 hasAuthorship W4378782647A5038065195 @default.
- W4378782647 hasAuthorship W4378782647A5078562310 @default.
- W4378782647 hasBestOaLocation W43787826471 @default.
- W4378782647 hasConcept C119857082 @default.
- W4378782647 hasConcept C124101348 @default.
- W4378782647 hasConcept C132525143 @default.
- W4378782647 hasConcept C154945302 @default.
- W4378782647 hasConcept C2781067378 @default.
- W4378782647 hasConcept C41008148 @default.
- W4378782647 hasConcept C80444323 @default.
- W4378782647 hasConceptScore W4378782647C119857082 @default.
- W4378782647 hasConceptScore W4378782647C124101348 @default.
- W4378782647 hasConceptScore W4378782647C132525143 @default.
- W4378782647 hasConceptScore W4378782647C154945302 @default.
- W4378782647 hasConceptScore W4378782647C2781067378 @default.
- W4378782647 hasConceptScore W4378782647C41008148 @default.
- W4378782647 hasConceptScore W4378782647C80444323 @default.
- W4378782647 hasFunder F4320321001 @default.
- W4378782647 hasFunder F4320322609 @default.
- W4378782647 hasIssue "6" @default.
- W4378782647 hasLocation W43787826471 @default.
- W4378782647 hasLocation W43787826472 @default.
- W4378782647 hasLocation W43787826473 @default.
- W4378782647 hasOpenAccess W4378782647 @default.
- W4378782647 hasPrimaryLocation W43787826471 @default.
- W4378782647 hasRelatedWork W1986582023 @default.
- W4378782647 hasRelatedWork W3006943036 @default.
- W4378782647 hasRelatedWork W4200511449 @default.
- W4378782647 hasRelatedWork W4206534706 @default.
- W4378782647 hasRelatedWork W4229079080 @default.
- W4378782647 hasRelatedWork W4299487748 @default.
- W4378782647 hasRelatedWork W4385767940 @default.
- W4378782647 hasRelatedWork W4385957992 @default.
- W4378782647 hasRelatedWork W4385965371 @default.
- W4378782647 hasRelatedWork W4386025632 @default.
- W4378782647 hasVolume "39" @default.
- W4378782647 isParatext "false" @default.
- W4378782647 isRetracted "false" @default.
- W4378782647 workType "article" @default.