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lazar: a modular predictive toxicology framework

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers ca...

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Detalles Bibliográficos
Autores principales: Maunz, Andreas, Gütlein, Martin, Rautenberg, Micha, Vorgrimmler, David, Gebele, Denis, Helma, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3669891/
https://www.ncbi.nlm.nih.gov/pubmed/23761761
http://dx.doi.org/10.3389/fphar.2013.00038
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author Maunz, Andreas
Gütlein, Martin
Rautenberg, Micha
Vorgrimmler, David
Gebele, Denis
Helma, Christoph
author_facet Maunz, Andreas
Gütlein, Martin
Rautenberg, Micha
Vorgrimmler, David
Gebele, Denis
Helma, Christoph
author_sort Maunz, Andreas
collection PubMed
description lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models.
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spelling pubmed-36698912013-06-11 lazar: a modular predictive toxicology framework Maunz, Andreas Gütlein, Martin Rautenberg, Micha Vorgrimmler, David Gebele, Denis Helma, Christoph Front Pharmacol Pharmacology lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models. Frontiers Media S.A. 2013-04-09 /pmc/articles/PMC3669891/ /pubmed/23761761 http://dx.doi.org/10.3389/fphar.2013.00038 Text en Copyright © Maunz, Gütlein, Rautenberg, Vorgrimmler, Gebele and Helma. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Pharmacology
Maunz, Andreas
Gütlein, Martin
Rautenberg, Micha
Vorgrimmler, David
Gebele, Denis
Helma, Christoph
lazar: a modular predictive toxicology framework
title lazar: a modular predictive toxicology framework
title_full lazar: a modular predictive toxicology framework
title_fullStr lazar: a modular predictive toxicology framework
title_full_unstemmed lazar: a modular predictive toxicology framework
title_short lazar: a modular predictive toxicology framework
title_sort lazar: a modular predictive toxicology framework
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3669891/
https://www.ncbi.nlm.nih.gov/pubmed/23761761
http://dx.doi.org/10.3389/fphar.2013.00038
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