ArticleBioMed research international2017
DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predicting drug protein interactions based on improved support vector data description in unbalanced data.BioImpacts : BI · 2025Article
- SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.BMC bioinformatics · 2023Article
- Drug-target interaction prediction via multiple classification strategies.BMC bioinformatics · 2022Article
- Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets.Diagnostics (Basel, Switzerland) · 2021Article
- Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.BMC bioinformatics · 2021Article
- Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition.International journal of molecular sciences · 2020Article
- Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities.Frontiers in chemistry · 2019Review
- Predicting circRNA-Disease Associations Based on circRNA Expression Similarity and Functional Similarity.Frontiers in genetics · 2019Article
- PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2018Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug-target interaction is key in drug discovery, especially in the design of new lead compound. However, the work to find a new lead compound for a specific target is complicated and hard, and it always leads to many mistakes. Therefore computational techniques are commonly adopted in drug design, which can save time and costs to a significant extent.
resultsTo address the issue, a new prediction system is proposed in this work to identify drug-target interaction. First, drug-target pairs are encoded with a fragment technique and the software "PaDEL-Descriptor." The fragment technique is for encoding target proteins, which divides each protein sequence into several fragments in order and encodes each fragment with several physiochemical properties of amino acids. The software "PaDEL-Descriptor" creates encoding vectors for drug molecules. Second, the dataset of drug-target pairs is resampled and several overlapped subsets are obtained, which are then input into kNN (
conclusionExperimental results on the drug-target dataset showed that our method performs better and runs faster than the state-of-the-art predictors.
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