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Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells

SK-MEL-5 is a human melanoma cell line that has been used in various studies to explore new therapies against melanoma in different in vitro experiments. Based on this study we report on the development of quantitative structure-activity relationship (QSAR) models able to predict the cytotoxic effec...

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Autores principales: Ancuceanu, Robert, Dinu, Mihaela, Neaga, Iana, Laszlo, Fekete Gyula, Boda, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466999/
https://www.ncbi.nlm.nih.gov/pubmed/31007759
http://dx.doi.org/10.3892/ol.2019.10068
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author Ancuceanu, Robert
Dinu, Mihaela
Neaga, Iana
Laszlo, Fekete Gyula
Boda, Daniel
author_facet Ancuceanu, Robert
Dinu, Mihaela
Neaga, Iana
Laszlo, Fekete Gyula
Boda, Daniel
author_sort Ancuceanu, Robert
collection PubMed
description SK-MEL-5 is a human melanoma cell line that has been used in various studies to explore new therapies against melanoma in different in vitro experiments. Based on this study we report on the development of quantitative structure-activity relationship (QSAR) models able to predict the cytotoxic effect of diverse chemical compounds on this cancer cell line. The dataset of cytotoxic and inactive compounds were downloaded from the PubChem database. It contains the data for all chemical compounds for which cytotoxicity results expressed by GI(50) was recorded. In total 13 blocks of molecular descriptors were computed and used, after appropriate pre-processing in building QSAR models with four machine learning classifiers: Random forest (RF), gradient boosting, support vector machine and random k-nearest neighbors. Among the 186 models reported none had a positive predictive value (PPV) higher than 0.90 in both nested cross-validation and on an external dataset testing, but 7 models had a PPV higher than 0.85 in both evaluations, all seven using the RFs algorithm as a classifier, and topological descriptors, information indices, 2D-autocorrelation descriptors, P-VSA-like descriptors, and edge-adjacency descriptors as sets of features used for classification. The y-scrambling test was associated with considerably worse performance (confirming the non-random character of the models) and the applicability domain was assessed through three different methods.
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spelling pubmed-64669992019-04-19 Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells Ancuceanu, Robert Dinu, Mihaela Neaga, Iana Laszlo, Fekete Gyula Boda, Daniel Oncol Lett Articles SK-MEL-5 is a human melanoma cell line that has been used in various studies to explore new therapies against melanoma in different in vitro experiments. Based on this study we report on the development of quantitative structure-activity relationship (QSAR) models able to predict the cytotoxic effect of diverse chemical compounds on this cancer cell line. The dataset of cytotoxic and inactive compounds were downloaded from the PubChem database. It contains the data for all chemical compounds for which cytotoxicity results expressed by GI(50) was recorded. In total 13 blocks of molecular descriptors were computed and used, after appropriate pre-processing in building QSAR models with four machine learning classifiers: Random forest (RF), gradient boosting, support vector machine and random k-nearest neighbors. Among the 186 models reported none had a positive predictive value (PPV) higher than 0.90 in both nested cross-validation and on an external dataset testing, but 7 models had a PPV higher than 0.85 in both evaluations, all seven using the RFs algorithm as a classifier, and topological descriptors, information indices, 2D-autocorrelation descriptors, P-VSA-like descriptors, and edge-adjacency descriptors as sets of features used for classification. The y-scrambling test was associated with considerably worse performance (confirming the non-random character of the models) and the applicability domain was assessed through three different methods. D.A. Spandidos 2019-05 2019-02-25 /pmc/articles/PMC6466999/ /pubmed/31007759 http://dx.doi.org/10.3892/ol.2019.10068 Text en Copyright: © Ancuceanu et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Ancuceanu, Robert
Dinu, Mihaela
Neaga, Iana
Laszlo, Fekete Gyula
Boda, Daniel
Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells
title Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells
title_full Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells
title_fullStr Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells
title_full_unstemmed Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells
title_short Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells
title_sort development of qsar machine learning-based models to forecast the effect of substances on malignant melanoma cells
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466999/
https://www.ncbi.nlm.nih.gov/pubmed/31007759
http://dx.doi.org/10.3892/ol.2019.10068
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