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Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives
An approach using multivariate adaptive regression splines (MARSplines) was applied for quantitative structure–activity relationship studies of the antitumor activity of anthrapyrazoles. At the first stage, the structures of anthrapyrazole derivatives were subjected to geometrical optimization by th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104800/ https://www.ncbi.nlm.nih.gov/pubmed/35563523 http://dx.doi.org/10.3390/ijms23095132 |
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author | Gackowski, Marcin Szewczyk-Golec, Karolina Pluskota, Robert Koba, Marcin Mądra-Gackowska, Katarzyna Woźniak, Alina |
author_facet | Gackowski, Marcin Szewczyk-Golec, Karolina Pluskota, Robert Koba, Marcin Mądra-Gackowska, Katarzyna Woźniak, Alina |
author_sort | Gackowski, Marcin |
collection | PubMed |
description | An approach using multivariate adaptive regression splines (MARSplines) was applied for quantitative structure–activity relationship studies of the antitumor activity of anthrapyrazoles. At the first stage, the structures of anthrapyrazole derivatives were subjected to geometrical optimization by the AM1 method using the Polak–Ribiere algorithm. In the next step, a data set of 73 compounds was coded over 2500 calculated molecular descriptors. It was shown that fourteen independent variables appearing in the statistically significant MARS model (i.e., descriptors belonging to 3D-MoRSE, 2D autocorrelations, GETAWAY, burden eigenvalues and RDF descriptors), significantly affect the antitumor activity of anthrapyrazole compounds. The study confirmed the benefit of using a modern machine learning algorithm, since the high predictive power of the obtained model had proven to be useful for the prediction of antitumor activity against murine leukemia L1210. It could certainly be considered as a tool for predicting activity against other cancer cell lines. |
format | Online Article Text |
id | pubmed-9104800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91048002022-05-14 Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives Gackowski, Marcin Szewczyk-Golec, Karolina Pluskota, Robert Koba, Marcin Mądra-Gackowska, Katarzyna Woźniak, Alina Int J Mol Sci Article An approach using multivariate adaptive regression splines (MARSplines) was applied for quantitative structure–activity relationship studies of the antitumor activity of anthrapyrazoles. At the first stage, the structures of anthrapyrazole derivatives were subjected to geometrical optimization by the AM1 method using the Polak–Ribiere algorithm. In the next step, a data set of 73 compounds was coded over 2500 calculated molecular descriptors. It was shown that fourteen independent variables appearing in the statistically significant MARS model (i.e., descriptors belonging to 3D-MoRSE, 2D autocorrelations, GETAWAY, burden eigenvalues and RDF descriptors), significantly affect the antitumor activity of anthrapyrazole compounds. The study confirmed the benefit of using a modern machine learning algorithm, since the high predictive power of the obtained model had proven to be useful for the prediction of antitumor activity against murine leukemia L1210. It could certainly be considered as a tool for predicting activity against other cancer cell lines. MDPI 2022-05-04 /pmc/articles/PMC9104800/ /pubmed/35563523 http://dx.doi.org/10.3390/ijms23095132 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gackowski, Marcin Szewczyk-Golec, Karolina Pluskota, Robert Koba, Marcin Mądra-Gackowska, Katarzyna Woźniak, Alina Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives |
title | Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives |
title_full | Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives |
title_fullStr | Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives |
title_full_unstemmed | Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives |
title_short | Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives |
title_sort | application of multivariate adaptive regression splines (marsplines) for predicting antitumor activity of anthrapyrazole derivatives |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104800/ https://www.ncbi.nlm.nih.gov/pubmed/35563523 http://dx.doi.org/10.3390/ijms23095132 |
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