ArticleBMC bioinformatics2023
DGDTA: dynamic graph attention network for predicting drug-target binding affinity.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 43 citations in OpenAlex.
- GraESM-FuseDTA: adaptive gated multimodal fusion of graph neural networks and protein language models for robust drug-target affinity prediction.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Hybrid Dual-Context Prompted Cross-Attention Framework with Language Model Guidance for Multi-Label Prediction of Human Off-Target Ligand-Protein Interactions.International journal of molecular sciences · 2026Article
- DTAP: a unified graph transformer framework for joint prediction of drug-target affinity and docking pose.Briefings in bioinformatics · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Predicting drug-target affinity through triple pre-activated random residual planet convolution coupled attention network and contact maps.Journal of computer-aided molecular design · 2025Article
- MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network.BMC genomics · 2025Article
- InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks.Heliyon · 2025Article
- GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity.BMC genomics · 2025Article
- Dual modality feature fused neural network integrating binding site information for drug target affinity prediction.NPJ digital medicine · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- Enhancing drug-target interaction prediction with graph representation learning and knowledge-based regularization.Frontiers in bioinformatics · 2025Article
- TPepPro: a deep learning model for predicting peptide-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks.BMC genomics · 2024Article
- Drug-target affinity prediction with extended graph learning-convolutional networks.BMC bioinformatics · 2024Article
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundObtaining accurate drug-target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive.
resultsDynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA .
conclusionsThe experimental results show that DGDTA can predict DTA more accurately than some other methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.