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Improving chemical similarity ensemble approach in target prediction

BACKGROUND: In silico target prediction of compounds plays an important role in drug discovery. The chemical similarity ensemble approach (SEA) is a promising method, which has been successfully applied in many drug-related studies. There are various models available analogous to SEA, because this a...

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Autores principales: Wang, Zhonghua, Liang, Lu, Yin, Zheng, Lin, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842302/
https://www.ncbi.nlm.nih.gov/pubmed/27110288
http://dx.doi.org/10.1186/s13321-016-0130-x
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author Wang, Zhonghua
Liang, Lu
Yin, Zheng
Lin, Jianping
author_facet Wang, Zhonghua
Liang, Lu
Yin, Zheng
Lin, Jianping
author_sort Wang, Zhonghua
collection PubMed
description BACKGROUND: In silico target prediction of compounds plays an important role in drug discovery. The chemical similarity ensemble approach (SEA) is a promising method, which has been successfully applied in many drug-related studies. There are various models available analogous to SEA, because this approach is based on different types of molecular fingerprints. To investigate the influence of training data selection and the complementarity of different models, several SEA models were constructed and tested. RESULTS: When we used a test set of 37,138 positive and 42,928 negative ligand-target interactions, among the five tested molecular fingerprint methods, at significance level 0.05, Topological-based model yielded the best precision rate (83.7 %) and [Formula: see text] (0.784) while Atom pair-based model yielded the best [Formula: see text] (0.694). By employing an election system to combine the five models, a flexible prediction scheme was achieved with precision range from 71 to 90.6 %, [Formula: see text] range from 0.663 to 0.684 and [Formula: see text] range from 0.696 to 0.817. CONCLUSIONS: The overall effectiveness of all of the five models could be ranked in decreasing order as follows: Atom pair [Formula: see text] Topological > Morgan > MACCS > Pharmacophore. Combining multiple SEA models, which takes advantages of different models, could be used to improve the success rates of the models. Another possibility of improving the model could be using target-specific classes or more active compounds. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-016-0130-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-48423022016-04-25 Improving chemical similarity ensemble approach in target prediction Wang, Zhonghua Liang, Lu Yin, Zheng Lin, Jianping J Cheminform Research Article BACKGROUND: In silico target prediction of compounds plays an important role in drug discovery. The chemical similarity ensemble approach (SEA) is a promising method, which has been successfully applied in many drug-related studies. There are various models available analogous to SEA, because this approach is based on different types of molecular fingerprints. To investigate the influence of training data selection and the complementarity of different models, several SEA models were constructed and tested. RESULTS: When we used a test set of 37,138 positive and 42,928 negative ligand-target interactions, among the five tested molecular fingerprint methods, at significance level 0.05, Topological-based model yielded the best precision rate (83.7 %) and [Formula: see text] (0.784) while Atom pair-based model yielded the best [Formula: see text] (0.694). By employing an election system to combine the five models, a flexible prediction scheme was achieved with precision range from 71 to 90.6 %, [Formula: see text] range from 0.663 to 0.684 and [Formula: see text] range from 0.696 to 0.817. CONCLUSIONS: The overall effectiveness of all of the five models could be ranked in decreasing order as follows: Atom pair [Formula: see text] Topological > Morgan > MACCS > Pharmacophore. Combining multiple SEA models, which takes advantages of different models, could be used to improve the success rates of the models. Another possibility of improving the model could be using target-specific classes or more active compounds. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-016-0130-x) contains supplementary material, which is available to authorized users. Springer International Publishing 2016-04-23 /pmc/articles/PMC4842302/ /pubmed/27110288 http://dx.doi.org/10.1186/s13321-016-0130-x Text en © Wang et al. 2016 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
Wang, Zhonghua
Liang, Lu
Yin, Zheng
Lin, Jianping
Improving chemical similarity ensemble approach in target prediction
title Improving chemical similarity ensemble approach in target prediction
title_full Improving chemical similarity ensemble approach in target prediction
title_fullStr Improving chemical similarity ensemble approach in target prediction
title_full_unstemmed Improving chemical similarity ensemble approach in target prediction
title_short Improving chemical similarity ensemble approach in target prediction
title_sort improving chemical similarity ensemble approach in target prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842302/
https://www.ncbi.nlm.nih.gov/pubmed/27110288
http://dx.doi.org/10.1186/s13321-016-0130-x
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