ArticlePLoS computational biology2016
Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction.
Article in PLoS computational biology, 2016. 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, 46 citations in OpenAlex.
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
- Investigation of the General Molecular Mechanisms of Gallic Acid via Analyses of Its Transcriptome Profile.International journal of molecular sciences · 2024Article
- Drug repositioning: A bibliometric analysis.Frontiers in pharmacology · 2022Review
- Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery.Current genomics · 2021Review
- Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach.Scientific reports · 2021Article
- PIMD: An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion.Genomics, proteomics & bioinformatics · 2020Article
- Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition.International journal of molecular sciences · 2020Article
- DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.Journal of cheminformatics · 2020Article
- Drug repurposing for antimicrobial discovery.Nature microbiology · 2019Review
- Effect of Ling-Gui-Zhu-Gan decoction major components on the plasma protein binding of metoprolol using UPLC analysis coupled with ultrafiltration.RSC advances · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Targeted drug discovery and development, from molecular signaling to the global market: an educational program at New York University, 5-year metrics.Journal of translational science · 2018Article
- Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.PLoS computational biology · 2017Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.
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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.