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Molecular Geometry Prediction using a Deep Generative Graph Neural Network

A molecule’s geometry, also known as conformation, is one of a molecule’s most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force...

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Autores principales: Mansimov, Elman, Mahmood, Omar, Kang, Seokho, Cho, Kyunghyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938476/
https://www.ncbi.nlm.nih.gov/pubmed/31892716
http://dx.doi.org/10.1038/s41598-019-56773-5
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author Mansimov, Elman
Mahmood, Omar
Kang, Seokho
Cho, Kyunghyun
author_facet Mansimov, Elman
Mahmood, Omar
Kang, Seokho
Cho, Kyunghyun
author_sort Mansimov, Elman
collection PubMed
description A molecule’s geometry, also known as conformation, is one of a molecule’s most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations.
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spelling pubmed-69384762020-01-06 Molecular Geometry Prediction using a Deep Generative Graph Neural Network Mansimov, Elman Mahmood, Omar Kang, Seokho Cho, Kyunghyun Sci Rep Article A molecule’s geometry, also known as conformation, is one of a molecule’s most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations. Nature Publishing Group UK 2019-12-31 /pmc/articles/PMC6938476/ /pubmed/31892716 http://dx.doi.org/10.1038/s41598-019-56773-5 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Mansimov, Elman
Mahmood, Omar
Kang, Seokho
Cho, Kyunghyun
Molecular Geometry Prediction using a Deep Generative Graph Neural Network
title Molecular Geometry Prediction using a Deep Generative Graph Neural Network
title_full Molecular Geometry Prediction using a Deep Generative Graph Neural Network
title_fullStr Molecular Geometry Prediction using a Deep Generative Graph Neural Network
title_full_unstemmed Molecular Geometry Prediction using a Deep Generative Graph Neural Network
title_short Molecular Geometry Prediction using a Deep Generative Graph Neural Network
title_sort molecular geometry prediction using a deep generative graph neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938476/
https://www.ncbi.nlm.nih.gov/pubmed/31892716
http://dx.doi.org/10.1038/s41598-019-56773-5
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