ArticleBMC bioinformatics2019
Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Prioritising search for virtual screening via preliminary interpretable low-feature likelihood-based rankings of drug-target activity measures.BMC bioinformatics · 2026Article
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- Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.BMC bioinformatics · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Improving drug repositioning with negative data labeling using large language models.Journal of cheminformatics · 2025Article
- Drug Repurposing: Insights into Current Advances and Future Applications.Current medicinal chemistry · 2025Review
- An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.Current computer-aided drug design · 2025Article
- Causal enhanced drug-target interaction prediction based on graph generation and multi-source information fusion.Bioinformatics (Oxford, England) · 2024Article
- Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection.International journal of molecular sciences · 2024Article
- CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.BMC bioinformatics · 2024Article
- Drug-target affinity prediction with extended graph learning-convolutional networks.BMC bioinformatics · 2024Article
- Techniques and Strategies in Drug Design and Discovery.International journal of molecular sciences · 2024Article
- Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.Current drug discovery technologies · 2024Review
- Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of theInternational journal of molecular sciences · 2023Article
- The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.GigaScience · 2022Article
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- Detecting Drug-Target Interactions with Feature Similarity Fusion and Molecular Graphs.Biology · 2022Article
- Sequence-based drug-target affinity prediction using weighted graph neural networks.BMC genomics · 2022Article
- Predicting molecular initiating events using chemical target annotations and gene expression.BioData mining · 2022Article
- Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.Methods in molecular biology (Clifton, N.J.) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many computational approaches have been developed for predicting drug-target associations on a large scale.
resultsIn this paper, we proposed an deep learning-based method to predict DTIs only using the information of drug structures and protein sequences. The final results showed that our method can achieve good performance with the accuracies up to 92.0%, 90.0%, 92.0% and 90.7% for the target families of enzymes, ion channels, GPCRs and nuclear receptors of our created dataset, respectively. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded an accuracy of 0.9015 and an AUC value of 0.9557.
conclusionIt was elucidated that our model shows improved performance in comparison with other state-of-the-art computational methods on the common benchmark datasets. Experimental results demonstrated that our model successfully extracted more nuanced yet useful features, and therefore can be used as a practical tool to discover new drugs. AVAILABILITY: http://deeplearner.ahu.edu.cn/web/CnnDTI.htm.
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