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Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug disc...

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Autores principales: Lim, Hansaim, Gray, Paul, Xie, Lei, Poleksic, Aleksandar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5153628/
https://www.ncbi.nlm.nih.gov/pubmed/27958331
http://dx.doi.org/10.1038/srep38860
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author Lim, Hansaim
Gray, Paul
Xie, Lei
Poleksic, Aleksandar
author_facet Lim, Hansaim
Gray, Paul
Xie, Lei
Poleksic, Aleksandar
author_sort Lim, Hansaim
collection PubMed
description Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.
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spelling pubmed-51536282016-12-19 Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem Lim, Hansaim Gray, Paul Xie, Lei Poleksic, Aleksandar Sci Rep Article Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design. Nature Publishing Group 2016-12-13 /pmc/articles/PMC5153628/ /pubmed/27958331 http://dx.doi.org/10.1038/srep38860 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Lim, Hansaim
Gray, Paul
Xie, Lei
Poleksic, Aleksandar
Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem
title Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem
title_full Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem
title_fullStr Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem
title_full_unstemmed Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem
title_short Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem
title_sort improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5153628/
https://www.ncbi.nlm.nih.gov/pubmed/27958331
http://dx.doi.org/10.1038/srep38860
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