ArticleBioinformatics (Oxford, England)2016
DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Heterogeneous Network With Multiview Path Aggregation: Drug-Target Interaction Prediction Study Design.JMIR medical informatics · 2025Article
- An Interpretable Deep Learning and Molecular Docking Framework for Repurposing Existing Drugs as Inhibitors of SARS-CoV-2 Main Protease.Molecules (Basel, Switzerland) · 2025Article
- Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury.Chinese medicine · 2025Article
- RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models.Journal of chemical information and modeling · 2024Article
- Identify potential drug candidates within a high-quality compound search space.Briefings in bioinformatics · 2024Article
- MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism.Frontiers in genetics · 2024Article
- AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion.International journal of molecular sciences · 2023Article
- Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.European journal of medicinal chemistry · 2023Review
- BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo.PLoS computational biology · 2023Article
- iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank.PLoS computational biology · 2022Article
- BETA: a comprehensive benchmark for computational drug-target prediction.Briefings in bioinformatics · 2022Article
- DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.Interdisciplinary sciences, computational life sciences · 2022Article
- LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions.Frontiers in microbiology · 2022Article
- NeuRank: learning to rank with neural networks for drug-target interaction prediction.BMC bioinformatics · 2021Article
- Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.Molecules (Basel, Switzerland) · 2021Article
- Efficient machine learning model for predicting drug-target interactions with case study for Covid-19.Computational biology and chemistry · 2021Article
- Accurate acid dissociation constant (pKJournal of molecular modeling · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationIdentifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets.
methodsApproaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning.
resultsThe performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. AVAILABILITY: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank CONTACT: zhusf@fudan.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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