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Extremely Randomized Machine Learning Methods for Compound Activity Prediction
Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332304/ https://www.ncbi.nlm.nih.gov/pubmed/26569196 http://dx.doi.org/10.3390/molecules201119679 |
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author | Czarnecki, Wojciech M. Podlewska, Sabina Bojarski, Andrzej J. |
author_facet | Czarnecki, Wojciech M. Podlewska, Sabina Bojarski, Andrzej J. |
author_sort | Czarnecki, Wojciech M. |
collection | PubMed |
description | Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure. |
format | Online Article Text |
id | pubmed-6332304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63323042019-01-24 Extremely Randomized Machine Learning Methods for Compound Activity Prediction Czarnecki, Wojciech M. Podlewska, Sabina Bojarski, Andrzej J. Molecules Article Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure. MDPI 2015-11-09 /pmc/articles/PMC6332304/ /pubmed/26569196 http://dx.doi.org/10.3390/molecules201119679 Text en © 2015 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Czarnecki, Wojciech M. Podlewska, Sabina Bojarski, Andrzej J. Extremely Randomized Machine Learning Methods for Compound Activity Prediction |
title | Extremely Randomized Machine Learning Methods for Compound Activity Prediction |
title_full | Extremely Randomized Machine Learning Methods for Compound Activity Prediction |
title_fullStr | Extremely Randomized Machine Learning Methods for Compound Activity Prediction |
title_full_unstemmed | Extremely Randomized Machine Learning Methods for Compound Activity Prediction |
title_short | Extremely Randomized Machine Learning Methods for Compound Activity Prediction |
title_sort | extremely randomized machine learning methods for compound activity prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332304/ https://www.ncbi.nlm.nih.gov/pubmed/26569196 http://dx.doi.org/10.3390/molecules201119679 |
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