ArticleBMC bioinformatics2021
AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction.PloS one · 2026Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model.Scientific reports · 2024Article
- Article
- LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening.Health information science and systems · 2023Article
- Prediction of the interaction betweenHeliyon · 2023Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- DEDTI versus IEDTI: efficient and predictive models of drug-target interactions.Scientific reports · 2023Article
- Matrix factorization with denoising autoencoders for prediction of drug-target interactions.Molecular diversity · 2023Article
- Drug-target interaction prediction based on protein features, using wrapper feature selection.Scientific reports · 2023Article
- DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.BMC bioinformatics · 2023Article
- Identification of potent compounds against SARs-CoV-2: An in-silico based drug searching against Mpro.Computers in biology and medicine · 2022Article
- OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets.BMC bioinformatics · 2022Article
- Drug repositioning based on heterogeneous networks and variational graph autoencoders.Frontiers in pharmacology · 2022Article
- ICAN: Interpretable cross-attention network for identifying drug and target protein interactions.PloS one · 2022Article
- Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.Frontiers in pharmacology · 2022Article
- Multi-Objective Drug Design Based on Graph-Fragment Molecular Representation and Deep Evolutionary Learning.Frontiers in pharmacology · 2022Article
- A brief review of protein-ligand interaction prediction.Computational and structural biotechnology journal · 2022Review
- LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions.Frontiers in microbiology · 2022Article
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4 authors.
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Abstract
backgroundDrug-target interaction (DTI) plays a vital role in drug discovery. Identifying drug-target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug-target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning.
resultsThis paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model.
conclusionsExperimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy.
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