ArticleInternational journal of molecular sciences2023
AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- MSCMF-DTB: a multi-scale cross-modal fusion framework for drug-target binding prediction.Scientific reports · 2026Article
- Drug-Target Interaction Prediction with PIGLET.bioRxiv : the preprint server for biology · 2026Article
- Article
- A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies.Briefings in bioinformatics · 2025Review
- An Interpretable Deep Learning and Molecular Docking Framework for Repurposing Existing Drugs as Inhibitors of SARS-CoV-2 Main Protease.Molecules (Basel, Switzerland) · 2025Article
- Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms.BMC biology · 2025Article
- MVSF-AB: accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning.Bioinformatics (Oxford, England) · 2025Article
- A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods.Frontiers in oral health · 2025Article
- NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism.International journal of molecular sciences · 2024Article
- Light gradient boosting-based prediction of quality of life among oral cancer-treated patients.BMC oral health · 2024Article
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- Techniques and Strategies in Drug Design and Discovery.International journal of molecular sciences · 2024Article
- MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism.Frontiers in genetics · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human,
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