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QSAR-Co-X: an open source toolkit for multitarget QSAR modelling
Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical condit...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048082/ https://www.ncbi.nlm.nih.gov/pubmed/33858509 http://dx.doi.org/10.1186/s13321-021-00508-0 |
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author | Halder, Amit Kumar Dias Soeiro Cordeiro, M. Natália |
author_facet | Halder, Amit Kumar Dias Soeiro Cordeiro, M. Natália |
author_sort | Halder, Amit Kumar |
collection | PubMed |
description | Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00508-0. |
format | Online Article Text |
id | pubmed-8048082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-80480822021-04-15 QSAR-Co-X: an open source toolkit for multitarget QSAR modelling Halder, Amit Kumar Dias Soeiro Cordeiro, M. Natália J Cheminform Software Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00508-0. Springer International Publishing 2021-04-15 /pmc/articles/PMC8048082/ /pubmed/33858509 http://dx.doi.org/10.1186/s13321-021-00508-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Halder, Amit Kumar Dias Soeiro Cordeiro, M. Natália QSAR-Co-X: an open source toolkit for multitarget QSAR modelling |
title | QSAR-Co-X: an open source toolkit for multitarget QSAR modelling |
title_full | QSAR-Co-X: an open source toolkit for multitarget QSAR modelling |
title_fullStr | QSAR-Co-X: an open source toolkit for multitarget QSAR modelling |
title_full_unstemmed | QSAR-Co-X: an open source toolkit for multitarget QSAR modelling |
title_short | QSAR-Co-X: an open source toolkit for multitarget QSAR modelling |
title_sort | qsar-co-x: an open source toolkit for multitarget qsar modelling |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048082/ https://www.ncbi.nlm.nih.gov/pubmed/33858509 http://dx.doi.org/10.1186/s13321-021-00508-0 |
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