ArticlePloS one2013
Predicting drug-target interactions using drug-drug interactions.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- PTB-DDI: An Accurate and Simple Framework for Drug-Drug Interaction Prediction Based on Pre-Trained Tokenizer and BiLSTM Model.International journal of molecular sciences · 2024Article
- Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN).BMC medical imaging · 2024Article
- Hierarchical multimodal self-attention-based graph neural network for DTI prediction.Briefings in bioinformatics · 2024Article
- Computational systems biology in disease modeling and control, review and perspectives.NPJ systems biology and applications · 2022Review
- HIDTI: integration of heterogeneous information to predict drug-target interactions.Scientific reports · 2022Article
- DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.Frontiers in genetics · 2022Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.Briefings in bioinformatics · 2019Review
- Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review.The AAPS journal · 2017Review
- In silico re-identification of properties of drug target proteins.BMC bioinformatics · 2017Article
- Combination therapeutics in complex diseases.Journal of cellular and molecular medicine · 2016Review
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects ([Formula: see text]) and pharmacological information ([Formula: see text]), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, [Formula: see text] data from the STITCH database, [Formula: see text] from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions.
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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.