ArticleJournal of cheminformatics2021
DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- MLDTA an Ensemble-Driven Multimodal Model with Dynamic Fusion for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 2026Article
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Unlocking the Antioxidant, Enzyme Inhibitory and Acaricidal Potential ofFood science & nutrition · 2025Article
- HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug-disease association prediction.BMC biology · 2025Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Causal enhanced drug-target interaction prediction based on graph generation and multi-source information fusion.Bioinformatics (Oxford, England) · 2024Article
- Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.Scientific reports · 2024Article
- GCARDTI: Drug-target interaction prediction based on a hybrid mechanism in drug SELFIES.Quantitative biology (Beijing, China) · 2024Article
- MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.Bioinformatics (Oxford, England) · 2024Article
- DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning.Journal of cheminformatics · 2024Article
- A deep learning model predicts the presence of diverse cancer types using circulating tumor cells.Scientific reports · 2023Article
- AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion.International journal of molecular sciences · 2023Article
- Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers.Nature communications · 2023Article
- OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.Frontiers in genetics · 2023Article
- Identifying potential drug-target interactions based on ensemble deep learning.Frontiers in aging neuroscience · 2023Article
- LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.Frontiers in genetics · 2023Article
- The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.GigaScience · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug-target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.