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The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix
BACKGROUND: The accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations. Since a number of diverse conformational sampling algorithm exist, which exhaustively generate enough conformers, howe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5364127/ https://www.ncbi.nlm.nih.gov/pubmed/29086188 http://dx.doi.org/10.1186/s13321-017-0208-0 |
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author | Kim, Hyoungrae Jang, Cheongyun Yadav, Dharmendra K. Kim, Mi-hyun |
author_facet | Kim, Hyoungrae Jang, Cheongyun Yadav, Dharmendra K. Kim, Mi-hyun |
author_sort | Kim, Hyoungrae |
collection | PubMed |
description | BACKGROUND: The accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations. Since a number of diverse conformational sampling algorithm exist, which exhaustively generate enough conformers, however model building methods relies on explicit number of common conformers. RESULTS: In this work, we have attempted to make clustering algorithms, which could find reasonable number of representative conformer ensembles automatically with asymmetric dissimilarity matrix generated from openeye tool kit. RMSD was the important descriptor (variable) of each column of the N × N matrix considered as N variables describing the relationship (network) between the conformer (in a row) and the other N conformers. This approach used to evaluate the performance of the well-known clustering algorithms by comparison in terms of generating representative conformer ensembles and test them over different matrix transformation functions considering the stability. In the network, the representative conformer group could be resampled for four kinds of algorithms with implicit parameters. The directed dissimilarity matrix becomes the only input to the clustering algorithms. CONCLUSIONS: Dunn index, Davies–Bouldin index, Eta-squared values and omega-squared values were used to evaluate the clustering algorithms with respect to the compactness and the explanatory power. The evaluation includes the reduction (abstraction) rate of the data, correlation between the sizes of the population and the samples, the computational complexity and the memory usage as well. Every algorithm could find representative conformers automatically without any user intervention, and they reduced the data to 14–19% of the original values within 1.13 s per sample at the most. The clustering methods are simple and practical as they are fast and do not ask for any explicit parameters. RCDTC presented the maximum Dunn and omega-squared values of the four algorithms in addition to consistent reduction rate between the population size and the sample size. The performance of the clustering algorithms was consistent over different transformation functions. Moreover, the clustering method can also be applied to molecular dynamics sampling simulation results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-017-0208-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5364127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-53641272017-04-10 The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix Kim, Hyoungrae Jang, Cheongyun Yadav, Dharmendra K. Kim, Mi-hyun J Cheminform Methodology BACKGROUND: The accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations. Since a number of diverse conformational sampling algorithm exist, which exhaustively generate enough conformers, however model building methods relies on explicit number of common conformers. RESULTS: In this work, we have attempted to make clustering algorithms, which could find reasonable number of representative conformer ensembles automatically with asymmetric dissimilarity matrix generated from openeye tool kit. RMSD was the important descriptor (variable) of each column of the N × N matrix considered as N variables describing the relationship (network) between the conformer (in a row) and the other N conformers. This approach used to evaluate the performance of the well-known clustering algorithms by comparison in terms of generating representative conformer ensembles and test them over different matrix transformation functions considering the stability. In the network, the representative conformer group could be resampled for four kinds of algorithms with implicit parameters. The directed dissimilarity matrix becomes the only input to the clustering algorithms. CONCLUSIONS: Dunn index, Davies–Bouldin index, Eta-squared values and omega-squared values were used to evaluate the clustering algorithms with respect to the compactness and the explanatory power. The evaluation includes the reduction (abstraction) rate of the data, correlation between the sizes of the population and the samples, the computational complexity and the memory usage as well. Every algorithm could find representative conformers automatically without any user intervention, and they reduced the data to 14–19% of the original values within 1.13 s per sample at the most. The clustering methods are simple and practical as they are fast and do not ask for any explicit parameters. RCDTC presented the maximum Dunn and omega-squared values of the four algorithms in addition to consistent reduction rate between the population size and the sample size. The performance of the clustering algorithms was consistent over different transformation functions. Moreover, the clustering method can also be applied to molecular dynamics sampling simulation results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-017-0208-0) contains supplementary material, which is available to authorized users. Springer International Publishing 2017-03-23 /pmc/articles/PMC5364127/ /pubmed/29086188 http://dx.doi.org/10.1186/s13321-017-0208-0 Text en © The Author(s) 2017 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 | Methodology Kim, Hyoungrae Jang, Cheongyun Yadav, Dharmendra K. Kim, Mi-hyun The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix |
title | The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix |
title_full | The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix |
title_fullStr | The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix |
title_full_unstemmed | The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix |
title_short | The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix |
title_sort | comparison of automated clustering algorithms for resampling representative conformer ensembles with rmsd matrix |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5364127/ https://www.ncbi.nlm.nih.gov/pubmed/29086188 http://dx.doi.org/10.1186/s13321-017-0208-0 |
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