Comprehensive ensemble in QSAR prediction for drug discovery

BACKGROUND: Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based mac...

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Autores principales: Kwon, Sunyoung, Bae, Ho, Jo, Jeonghee, Yoon, Sungroh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6815455/
https://www.ncbi.nlm.nih.gov/pubmed/31655545
http://dx.doi.org/10.1186/s12859-019-3135-4
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author Kwon, Sunyoung
Bae, Ho
Jo, Jeonghee
Yoon, Sungroh
author_facet Kwon, Sunyoung
Bae, Ho
Jo, Jeonghee
Yoon, Sungroh
author_sort Kwon, Sunyoung
collection PubMed
description BACKGROUND: Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject. RESULTS: The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/. CONCLUSIONS: We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.
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spelling pubmed-68154552019-10-31 Comprehensive ensemble in QSAR prediction for drug discovery Kwon, Sunyoung Bae, Ho Jo, Jeonghee Yoon, Sungroh BMC Bioinformatics Methodology Article BACKGROUND: Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject. RESULTS: The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/. CONCLUSIONS: We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning. BioMed Central 2019-10-26 /pmc/articles/PMC6815455/ /pubmed/31655545 http://dx.doi.org/10.1186/s12859-019-3135-4 Text en © The Author(s) 2019 Open Access This 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 Methodology Article
Kwon, Sunyoung
Bae, Ho
Jo, Jeonghee
Yoon, Sungroh
Comprehensive ensemble in QSAR prediction for drug discovery
title Comprehensive ensemble in QSAR prediction for drug discovery
title_full Comprehensive ensemble in QSAR prediction for drug discovery
title_fullStr Comprehensive ensemble in QSAR prediction for drug discovery
title_full_unstemmed Comprehensive ensemble in QSAR prediction for drug discovery
title_short Comprehensive ensemble in QSAR prediction for drug discovery
title_sort comprehensive ensemble in qsar prediction for drug discovery
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6815455/
https://www.ncbi.nlm.nih.gov/pubmed/31655545
http://dx.doi.org/10.1186/s12859-019-3135-4
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