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ContraDRG: Automatic Partial Charge Prediction by Machine Learning

In recent years, machine learning techniques have been widely used in biomedical research to predict unseen data based on models trained on experimentally derived data. In the current study, we used machine learning algorithms to emulate computationally complex predictions in a reverse engineering–l...

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Detalles Bibliográficos
Autores principales: Martin, Roman, Heider, Dominik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831742/
https://www.ncbi.nlm.nih.gov/pubmed/31737032
http://dx.doi.org/10.3389/fgene.2019.00990
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author Martin, Roman
Heider, Dominik
author_facet Martin, Roman
Heider, Dominik
author_sort Martin, Roman
collection PubMed
description In recent years, machine learning techniques have been widely used in biomedical research to predict unseen data based on models trained on experimentally derived data. In the current study, we used machine learning algorithms to emulate computationally complex predictions in a reverse engineering–like manner and developed ContraDRG, a software that can be used to predict partial charges for small molecules based on PRODRG and Automated Topology Builder (ATB) predictions. Both tools generate molecular topology files, including the partial atomic charge, by using different procedures. We show that ContraDRG can accurately predict partial charges in a fraction of the time, because it exploits existing complex models with intensive calculations by using machine learning techniques and thus can also be applied for screening projects with large amounts of molecules. We provide ContraDRG as a web server, which can be used to automatically assign partial charges to incoming user-specified molecules by using our machine learning models. In this study, we compared ContraDRG with PRODRG and ATB in regard of predictivity by statistical methods. ContraDRG allows predicting ATB-derived partial charges with an R(2) value up to 0.980 and for PRODRG up to 1.00. While ATB requires hours or days for the quantum mechanical accurate calculation and refinements, ContraDRG does its approximation within seconds.
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spelling pubmed-68317422019-11-15 ContraDRG: Automatic Partial Charge Prediction by Machine Learning Martin, Roman Heider, Dominik Front Genet Genetics In recent years, machine learning techniques have been widely used in biomedical research to predict unseen data based on models trained on experimentally derived data. In the current study, we used machine learning algorithms to emulate computationally complex predictions in a reverse engineering–like manner and developed ContraDRG, a software that can be used to predict partial charges for small molecules based on PRODRG and Automated Topology Builder (ATB) predictions. Both tools generate molecular topology files, including the partial atomic charge, by using different procedures. We show that ContraDRG can accurately predict partial charges in a fraction of the time, because it exploits existing complex models with intensive calculations by using machine learning techniques and thus can also be applied for screening projects with large amounts of molecules. We provide ContraDRG as a web server, which can be used to automatically assign partial charges to incoming user-specified molecules by using our machine learning models. In this study, we compared ContraDRG with PRODRG and ATB in regard of predictivity by statistical methods. ContraDRG allows predicting ATB-derived partial charges with an R(2) value up to 0.980 and for PRODRG up to 1.00. While ATB requires hours or days for the quantum mechanical accurate calculation and refinements, ContraDRG does its approximation within seconds. Frontiers Media S.A. 2019-10-30 /pmc/articles/PMC6831742/ /pubmed/31737032 http://dx.doi.org/10.3389/fgene.2019.00990 Text en Copyright © 2019 Martin and Heider http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Martin, Roman
Heider, Dominik
ContraDRG: Automatic Partial Charge Prediction by Machine Learning
title ContraDRG: Automatic Partial Charge Prediction by Machine Learning
title_full ContraDRG: Automatic Partial Charge Prediction by Machine Learning
title_fullStr ContraDRG: Automatic Partial Charge Prediction by Machine Learning
title_full_unstemmed ContraDRG: Automatic Partial Charge Prediction by Machine Learning
title_short ContraDRG: Automatic Partial Charge Prediction by Machine Learning
title_sort contradrg: automatic partial charge prediction by machine learning
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831742/
https://www.ncbi.nlm.nih.gov/pubmed/31737032
http://dx.doi.org/10.3389/fgene.2019.00990
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