ArticleFrontiers in genetics2021
GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Graph convolution network based on meta-paths and mutual information for drug-target interaction prediction.BMC bioinformatics · 2025Article
- DHGT-DTI: Advancing drug-target interaction prediction through a dual-view heterogeneous network with GraphSAGE and Graph Transformer.Journal of pharmaceutical analysis · 2025Article
- MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction.Interdisciplinary sciences, computational life sciences · 2025Article
- Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.BMC bioinformatics · 2025Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.Journal of pharmaceutical analysis · 2025Review
- MFCADTI: improving drug-target interaction prediction by integrating multiple feature through cross attention mechanism.BMC bioinformatics · 2025Article
- Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model.Scientific reports · 2024Article
- A review of deep learning methods for ligand based drug virtual screening.Fundamental research · 2024Review
- Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.Current computer-aided drug design · 2024Article
- From intuition to AI: evolution of small molecule representations in drug discovery.Briefings in bioinformatics · 2023Article
- Graph regularized non-negative matrix factorization withBMC bioinformatics · 2023Article
- Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder.BMC bioinformatics · 2023Article
- Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction.BMC bioinformatics · 2022Article
- A brief review of protein-ligand interaction prediction.Computational and structural biotechnology journal · 2022Review
- Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction.Frontiers in genetics · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying drug-target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision-recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI.
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