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ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

BACKGROUND: Determination of acute toxicity, expressed as median lethal dose (LD(50)), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction...

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Autores principales: Lei, Tailong, Li, Youyong, Song, Yunlong, Li, Dan, Sun, Huiyong, Hou, Tingjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736633/
https://www.ncbi.nlm.nih.gov/pubmed/26839598
http://dx.doi.org/10.1186/s13321-016-0117-7
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author Lei, Tailong
Li, Youyong
Song, Yunlong
Li, Dan
Sun, Huiyong
Hou, Tingjun
author_facet Lei, Tailong
Li, Youyong
Song, Yunlong
Li, Dan
Sun, Huiyong
Hou, Tingjun
author_sort Lei, Tailong
collection PubMed
description BACKGROUND: Determination of acute toxicity, expressed as median lethal dose (LD(50)), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity. RESULTS: In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD(50) values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by q(ext)(2) for the test set containing 2376 molecules, ranged from 0.572 to 0.659. CONCLUSION: Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (q(ext)(2) = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity. [Figure: see text]
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spelling pubmed-47366332016-02-03 ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling Lei, Tailong Li, Youyong Song, Yunlong Li, Dan Sun, Huiyong Hou, Tingjun J Cheminform Research Article BACKGROUND: Determination of acute toxicity, expressed as median lethal dose (LD(50)), is one of the most important steps in drug discovery pipeline. Because in vivo assays for oral acute toxicity in mammals are time-consuming and costly, there is thus an urgent need to develop in silico prediction models of oral acute toxicity. RESULTS: In this study, based on a comprehensive data set containing 7314 diverse chemicals with rat oral LD(50) values, relevance vector machine (RVM) technique was employed to build the regression models for the prediction of oral acute toxicity in rate, which were compared with those built using other six machine learning approaches, including k-nearest-neighbor regression, random forest (RF), support vector machine, local approximate Gaussian process, multilayer perceptron ensemble, and eXtreme gradient boosting. A subset of the original molecular descriptors and structural fingerprints (PubChem or SubFP) was chosen by the Chi squared statistics. The prediction capabilities of individual QSAR models, measured by q(ext)(2) for the test set containing 2376 molecules, ranged from 0.572 to 0.659. CONCLUSION: Considering the overall prediction accuracy for the test set, RVM with Laplacian kernel and RF were recommended to build in silico models with better predictivity for rat oral acute toxicity. By combining the predictions from individual models, four consensus models were developed, yielding better prediction capabilities for the test set (q(ext)(2) = 0.669–0.689). Finally, some essential descriptors and substructures relevant to oral acute toxicity were identified and analyzed, and they may be served as property or substructure alerts to avoid toxicity. We believe that the best consensus model with high prediction accuracy can be used as a reliable virtual screening tool to filter out compounds with high rat oral acute toxicity. [Figure: see text] Springer International Publishing 2016-02-01 /pmc/articles/PMC4736633/ /pubmed/26839598 http://dx.doi.org/10.1186/s13321-016-0117-7 Text en © Lei 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
Lei, Tailong
Li, Youyong
Song, Yunlong
Li, Dan
Sun, Huiyong
Hou, Tingjun
ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
title ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
title_full ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
title_fullStr ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
title_full_unstemmed ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
title_short ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
title_sort admet evaluation in drug discovery: 15. accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736633/
https://www.ncbi.nlm.nih.gov/pubmed/26839598
http://dx.doi.org/10.1186/s13321-016-0117-7
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