ArticleBriefings in bioinformatics2021
Deep drug-target binding affinity prediction with multiple attention blocks.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- TSEDTA: a transformer-based neural network with SMILES transformer and ESM2 embeddings for drug-target binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- MSCMF-DTB: a multi-scale cross-modal fusion framework for drug-target binding prediction.Scientific reports · 2026Article
- DualPG-DTA: A Large Language Model-Powered Graph Neural Network Framework for Enhanced Drug-Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting In Vivo Anti-Leukemia Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- NTMFF-DTA: Prediction of Drug-Target Affinity Based on Network Topology and Multi-feature Fusion.Interdisciplinary sciences, computational life sciences · 2025Article
- In silico methods for drug-target interaction prediction.Cell reports methods · 2025Review
- A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction.Communications chemistry · 2025Article
- Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.ACS omega · 2025Article
- MultiChem: predicting chemical properties using multi-view graph attention network.BioData mining · 2025Article
- Drug target affinity prediction based on multi-scale gated power graph and multi-head linear attention mechanism.PloS one · 2025Article
- Auxiliary Diagnosis of Pulmonary Nodules' Benignancy and Malignancy Based on Machine Learning: A Retrospective Study.Journal of multidisciplinary healthcare · 2025Article
- RETRACTED: Optimizing chemotherapeutic targets in non-small cell lung cancer with transfer learning for precision medicine.PloS one · 2025Article
- Improving drug-target affinity prediction by adaptive self-supervised learning.PeerJ. Computer science · 2025Article
- Structure-inclusive similarity based directed GNN: a method that can control information flow to predict drug-target binding affinity.Bioinformatics (Oxford, England) · 2024Article
- MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction.Journal of chemical information and modeling · 2024Article
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Prediction of Drug-Target Affinity Using Attention Neural Network.International journal of molecular sciences · 2024Article
- DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.Bioinformatics (Oxford, England) · 2024Article
- DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning.Journal of cheminformatics · 2024Article
- StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.Frontiers in pharmacology · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value $k \in \{3, 5\}$. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and $3137$ FDA-approved drugs.
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