ArticleBiomolecules2021
EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction.
Article in Biomolecules, 2021. 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, 31 citations in OpenAlex.
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review.npj biomedical innovations · 2025Review
- Improving generalizability of drug-target binding prediction by pre-trained multi-view molecular representations.Bioinformatics (Oxford, England) · 2024Article
- Can large language models understand molecules?BMC bioinformatics · 2024Article
- A microphysiological system reveals neutrophil contact-dependent attenuation of pancreatic tumor progression by CXCR2 inhibition-based immunotherapy.Scientific reports · 2024Article
- Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX.Communications chemistry · 2024Article
- MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.Briefings in bioinformatics · 2024Article
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- 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
- Molecular Property Prediction by Combining LSTM and GAT.Biomolecules · 2023Article
- Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence-I.Biomolecules · 2022Article
- A brief review of protein-ligand interaction prediction.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs. However, due to the difficulty in extracting useful information from molecules, the performance of these methods is limited by the representation of drugs and target proteins. This study proposes a new model called EmbedDTI to enhance the representation of both drugs and target proteins, and improve the performance of DTI prediction. For protein sequences, we leverage language modeling for pretraining the feature embeddings of amino acids and feed them to a convolutional neural network model for further representation learning. For drugs, we build two levels of graphs to represent compound structural information, namely the atom graph and substructure graph, and adopt graph convolutional network with an attention module to learn the embedding vectors for the graphs. We compare EmbedDTI with the existing DTI predictors on two benchmark datasets. The experimental results show that EmbedDTI outperforms the state-of-the-art models, and the attention module can identify the components crucial for DTIs in compounds.
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