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A new chemoinformatics approach with improved strategies for effective predictions of potential drugs

BACKGROUND: Fast and accurate identification of potential drug candidates against therapeutic targets (i.e., drug–target interactions, DTIs) is a fundamental step in the early drug discovery process. However, experimental determination of DTIs is time-consuming and costly, especially for testing the...

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Autores principales: Hao, Ming, Bryant, Stephen H., Wang, Yanli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755712/
https://www.ncbi.nlm.nih.gov/pubmed/30311095
http://dx.doi.org/10.1186/s13321-018-0303-x
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author Hao, Ming
Bryant, Stephen H.
Wang, Yanli
author_facet Hao, Ming
Bryant, Stephen H.
Wang, Yanli
author_sort Hao, Ming
collection PubMed
description BACKGROUND: Fast and accurate identification of potential drug candidates against therapeutic targets (i.e., drug–target interactions, DTIs) is a fundamental step in the early drug discovery process. However, experimental determination of DTIs is time-consuming and costly, especially for testing the associations between the entire chemical and genomic spaces. Therefore, computationally efficient algorithms with accurate predictions are required to achieve such a challenging task. In this work, we design a new chemoinformatics approach derived from neighbor-based collaborative filtering (NBCF) to infer potential drug candidates for targets of interest. One of the fundamental steps of NBCF in the application of DTI predictions is to accurately measure the similarity between drugs solely based on the DTI profiles of known knowledge. However, commonly used similarity calculation methods such as COSINE may be noise-prone due to the extremely sparse property of the DTI bipartite network, which decreases the model performance of NBCF. We herein propose three strategies to remedy such a dilemma, which include: (1) adopting a positive pointwise mutual information (PPMI)-based similarity metric, which is noise-immune to some extent; (2) performing low-rank approximation of the original prediction scores; (3) incorporating auxiliary (complementary) information to produce the final predictions. RESULTS: We test the proposed methods in three benchmark datasets and the results indicate that our strategies are helpful to improve the NBCF performance for DTI predictions. Comparing to the prior algorithm, our methods exhibit better results assessed by a recall-based evaluation metric. CONCLUSIONS: A new chemoinformatics approach with improved strategies was successfully developed to predict potential DTIs. Among them, the model based on the sparsity resistant PPMI similarity metric exhibits the best performance, which may be helpful to researchers for identifying potential drugs against therapeutic targets of interest, and can also be applied to related research such as identifying candidate disease genes.
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spelling pubmed-67557122019-09-26 A new chemoinformatics approach with improved strategies for effective predictions of potential drugs Hao, Ming Bryant, Stephen H. Wang, Yanli J Cheminform Research Article BACKGROUND: Fast and accurate identification of potential drug candidates against therapeutic targets (i.e., drug–target interactions, DTIs) is a fundamental step in the early drug discovery process. However, experimental determination of DTIs is time-consuming and costly, especially for testing the associations between the entire chemical and genomic spaces. Therefore, computationally efficient algorithms with accurate predictions are required to achieve such a challenging task. In this work, we design a new chemoinformatics approach derived from neighbor-based collaborative filtering (NBCF) to infer potential drug candidates for targets of interest. One of the fundamental steps of NBCF in the application of DTI predictions is to accurately measure the similarity between drugs solely based on the DTI profiles of known knowledge. However, commonly used similarity calculation methods such as COSINE may be noise-prone due to the extremely sparse property of the DTI bipartite network, which decreases the model performance of NBCF. We herein propose three strategies to remedy such a dilemma, which include: (1) adopting a positive pointwise mutual information (PPMI)-based similarity metric, which is noise-immune to some extent; (2) performing low-rank approximation of the original prediction scores; (3) incorporating auxiliary (complementary) information to produce the final predictions. RESULTS: We test the proposed methods in three benchmark datasets and the results indicate that our strategies are helpful to improve the NBCF performance for DTI predictions. Comparing to the prior algorithm, our methods exhibit better results assessed by a recall-based evaluation metric. CONCLUSIONS: A new chemoinformatics approach with improved strategies was successfully developed to predict potential DTIs. Among them, the model based on the sparsity resistant PPMI similarity metric exhibits the best performance, which may be helpful to researchers for identifying potential drugs against therapeutic targets of interest, and can also be applied to related research such as identifying candidate disease genes. Springer International Publishing 2018-10-11 /pmc/articles/PMC6755712/ /pubmed/30311095 http://dx.doi.org/10.1186/s13321-018-0303-x Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Hao, Ming
Bryant, Stephen H.
Wang, Yanli
A new chemoinformatics approach with improved strategies for effective predictions of potential drugs
title A new chemoinformatics approach with improved strategies for effective predictions of potential drugs
title_full A new chemoinformatics approach with improved strategies for effective predictions of potential drugs
title_fullStr A new chemoinformatics approach with improved strategies for effective predictions of potential drugs
title_full_unstemmed A new chemoinformatics approach with improved strategies for effective predictions of potential drugs
title_short A new chemoinformatics approach with improved strategies for effective predictions of potential drugs
title_sort new chemoinformatics approach with improved strategies for effective predictions of potential drugs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755712/
https://www.ncbi.nlm.nih.gov/pubmed/30311095
http://dx.doi.org/10.1186/s13321-018-0303-x
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