ArticleInternational journal of molecular sciences2024
Prediction of Drug-Target Affinity Using Attention Neural Network.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Confidence-Gated Triage: Coupling Drug-Target Affinity and ADME-T Predictions to Prioritise Compounds for Docking.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- KANPM-DTA: improving drug-target affinity prediction with Kolmogorov-Arnold networks and pretrained models.Briefings in bioinformatics · 2026Article
- RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction.PloS one · 2026Article
- A Multi-modal Drug Target Affinity Prediction Based on Graph Features and Pre-trained Sequence Embeddings.Interdisciplinary sciences, computational life sciences · 2025Article
- 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
- DHGT-DTI: Advancing drug-target interaction prediction through a dual-view heterogeneous network with GraphSAGE and Graph Transformer.Journal of pharmaceutical analysis · 2025Article
- A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies.Briefings in bioinformatics · 2025Review
- NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism.International journal of molecular sciences · 2024Article
- Drug-Target Interaction Prediction Based on an Interactive Inference Network.International journal of molecular sciences · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.
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