DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation

Abstract: DP4+ is one of the most popular methods for the structure elucidation of natural products using NMR calculations. While the method is simple and easy to implement, it requires a series of procedures that can be tedious, coupled with the fact that its computational demand can be high in...

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Autores principales: Franco, Bruno A., Luciano, Ezequiel R., Sarotti, Ariel M., Zanardi, María M.
Formato: Artículo
Lenguaje:Inglés
Publicado: ASC 2023
Materias:
QUIMICA COMPUTACIONAL
DP4
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/17239
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139-123456789-17239
record_format dspace
spelling I33-R139-123456789-172392023-10-06T12:24:50Z DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation Franco, Bruno A. Luciano, Ezequiel R. Sarotti, Ariel M. Zanardi, María M. QUIMICA COMPUTACIONAL DP4 Abstract: DP4+ is one of the most popular methods for the structure elucidation of natural products using NMR calculations. While the method is simple and easy to implement, it requires a series of procedures that can be tedious, coupled with the fact that its computational demand can be high in certain cases. In this work, we made a substantial improvement to these limitations. First, we deeply explored the effect of molecular mechanics architecture on the DP4+ formalism (MM-DP4+). In addition, a Python applet (DP4+App) was developed to automate the entire process, requiring only the Gaussian NMR output files and a spreadsheet containing the experimental NMR data and labels. The script is designed to use the statistical parameters from the original 24 levels of theory (employing B3LYP/6-31G* geometries) and the new 36 levels explored in this work (over MMFF geometries). Furthermore, it enables the development of customizable methods using any desired level of theory, allowing for a free choice of test molecules. 2023-10-04T22:27:45Z 2023-10-04T22:27:45Z 2023 Artículo Franco, B. A. DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation [en línea]. Journal of Natural Products. 2023. doi: 10.1021/acs.jnatprod.3c00566. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/17239 0163-3864 https://repositorio.uca.edu.ar/handle/123456789/17239 10.1021/acs.jnatprod.3c00566 eng Acceso abierto http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ASC Journal of Natural Products. 2023.
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic QUIMICA COMPUTACIONAL
DP4
spellingShingle QUIMICA COMPUTACIONAL
DP4
Franco, Bruno A.
Luciano, Ezequiel R.
Sarotti, Ariel M.
Zanardi, María M.
DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation
topic_facet QUIMICA COMPUTACIONAL
DP4
description Abstract: DP4+ is one of the most popular methods for the structure elucidation of natural products using NMR calculations. While the method is simple and easy to implement, it requires a series of procedures that can be tedious, coupled with the fact that its computational demand can be high in certain cases. In this work, we made a substantial improvement to these limitations. First, we deeply explored the effect of molecular mechanics architecture on the DP4+ formalism (MM-DP4+). In addition, a Python applet (DP4+App) was developed to automate the entire process, requiring only the Gaussian NMR output files and a spreadsheet containing the experimental NMR data and labels. The script is designed to use the statistical parameters from the original 24 levels of theory (employing B3LYP/6-31G* geometries) and the new 36 levels explored in this work (over MMFF geometries). Furthermore, it enables the development of customizable methods using any desired level of theory, allowing for a free choice of test molecules.
format Artículo
author Franco, Bruno A.
Luciano, Ezequiel R.
Sarotti, Ariel M.
Zanardi, María M.
author_facet Franco, Bruno A.
Luciano, Ezequiel R.
Sarotti, Ariel M.
Zanardi, María M.
author_sort Franco, Bruno A.
title DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation
title_short DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation
title_full DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation
title_fullStr DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation
title_full_unstemmed DP4+App: Finding the Best Balance between Computational Cost and Predictive Capacity in the Structure Elucidation Process by DP4+. Factors Analysis and Automation
title_sort dp4+app: finding the best balance between computational cost and predictive capacity in the structure elucidation process by dp4+. factors analysis and automation
publisher ASC
publishDate 2023
url https://repositorio.uca.edu.ar/handle/123456789/17239
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