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AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis
Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202188/ https://www.ncbi.nlm.nih.gov/pubmed/35706060 http://dx.doi.org/10.1186/s13321-022-00618-3 |
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author | Isamura, Bienfait K. Lobb, Kevin A. |
author_facet | Isamura, Bienfait K. Lobb, Kevin A. |
author_sort | Isamura, Bienfait K. |
collection | PubMed |
description | Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to generate any Diels–Alder (DA) transition state geometry (TS) and analyze determined IRC paths in a (quasi-)automated fashion, given the product SMILES. Two modules of the package are devoted to performing, from IRC paths, reaction force analyses (RFA) and atomic (fragment) decompositions of the reaction force F and reaction force constant [Formula: see text] . The performance of the protocol has been assessed using a dataset of 2000 DA cycloadducts retrieved from the ZINC database. The sequential location of the corresponding TSs was achieved with a success rate of 95%. RFA plots confirmed the reaction force constant [Formula: see text] to be a good indicator of the (non)synchronicity of the associated DA reactions. Moreover, the atomic decomposition of [Formula: see text] allows for the rationalization of the (a)synchronicity of each DA reaction in terms of contributions stemming from pairs of interacting atoms. The source code of the AMADAR tool is available on GitHub [CMCDD/AMADAR(github.com)] and can be used directly with minor customizations, mostly regarding the local working environment of the user. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00618-3. |
format | Online Article Text |
id | pubmed-9202188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92021882022-06-17 AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis Isamura, Bienfait K. Lobb, Kevin A. J Cheminform Software Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention. This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to generate any Diels–Alder (DA) transition state geometry (TS) and analyze determined IRC paths in a (quasi-)automated fashion, given the product SMILES. Two modules of the package are devoted to performing, from IRC paths, reaction force analyses (RFA) and atomic (fragment) decompositions of the reaction force F and reaction force constant [Formula: see text] . The performance of the protocol has been assessed using a dataset of 2000 DA cycloadducts retrieved from the ZINC database. The sequential location of the corresponding TSs was achieved with a success rate of 95%. RFA plots confirmed the reaction force constant [Formula: see text] to be a good indicator of the (non)synchronicity of the associated DA reactions. Moreover, the atomic decomposition of [Formula: see text] allows for the rationalization of the (a)synchronicity of each DA reaction in terms of contributions stemming from pairs of interacting atoms. The source code of the AMADAR tool is available on GitHub [CMCDD/AMADAR(github.com)] and can be used directly with minor customizations, mostly regarding the local working environment of the user. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00618-3. Springer International Publishing 2022-06-15 /pmc/articles/PMC9202188/ /pubmed/35706060 http://dx.doi.org/10.1186/s13321-022-00618-3 Text en © The Author(s) 2022 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 Isamura, Bienfait K. Lobb, Kevin A. AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis |
title | AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis |
title_full | AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis |
title_fullStr | AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis |
title_full_unstemmed | AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis |
title_short | AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis |
title_sort | amadar: a python-based package for large scale prediction of diels–alder transition state geometries and irc path analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202188/ https://www.ncbi.nlm.nih.gov/pubmed/35706060 http://dx.doi.org/10.1186/s13321-022-00618-3 |
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