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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
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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. |
format | Online Article Text |
id | pubmed-3669891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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