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|a 10.1002/jcc.27349
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|a eng
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|a Margraf, Johannes T
|e verfasserin
|4 aut
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|a Neural graph distance embedding for molecular geometry generation
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 20.07.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.
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|a This article introduces neural graph distance embedding (nGDE), a method for generating 3D molecular geometries. Leveraging a graph neural network trained on the OE62 dataset of molecular geometries, nGDE predicts interatomic distances based on molecular graphs. These distances are then used in multidimensional scaling to produce 3D geometries, subsequently refined with standard bioorganic forcefields. The machine learning-based graph distance introduced herein is found to be an improvement over the conventional shortest path distances used in graph drawing. Comparative analysis with a state-of-the-art distance geometry method demonstrates nGDE's competitive performance, particularly showcasing robustness in handling polycyclic molecules-a challenge for existing methods
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|a Journal Article
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|a conformers
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|a geometry prediction
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|a graph neural network
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| 650 |
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|a machine learning
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 45(2024), 21 vom: 05. Aug., Seite 1784-1790
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnas
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|g volume:45
|g year:2024
|g number:21
|g day:05
|g month:08
|g pages:1784-1790
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|u http://dx.doi.org/10.1002/jcc.27349
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