ArticleScientific reports2022
DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Protein-protein interaction-mediated signaling networks in ischemic stroke: from molecular mechanisms to therapeutic strategies.Acta pharmacologica Sinica · 2026Review
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.Scientific reports · 2026Article
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AI-driven protein pocket detection through integrating deep Q-networks for structural analysis.Journal of computer-aided molecular design · 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
- LncRNA-Protein Interactions: A Key to Deciphering LncRNA Mechanisms.Biomolecules · 2025Review
- Predicting drug-target interactions using machine learning with improved data balancing and feature engineering.Scientific reports · 2025Article
- Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.Interdisciplinary sciences, computational life sciences · 2025Article
- SP-DTI: subpocket-informed transformer for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Convergent Protocols for Computing Protein-Ligand Interaction Energies Using Fragment-Based Quantum Chemistry.Journal of chemical theory and computation · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.Molecular informatics · 2024Article
- G-Computational and structural biotechnology journal · 2024Article
- Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.Briefings in bioinformatics · 2024Article
- Article
- Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.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
- Venom-derived peptides for breaking through the glass ceiling of drug development.Frontiers in chemistry · 2024Review
- Using alternative SMILES representations to identify novel functional analogues in chemical similarity vector searches.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein-ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein-ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein-ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein-ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.
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