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Neural graph distance embedding for molecular geometry generation

Title data

Margraf, Johannes T.:
Neural graph distance embedding for molecular geometry generation.
In: Journal of Computational Chemistry. Vol. 45 (2024) Issue 21 . - pp. 1784-1790.
ISSN 1096-987X
DOI: https://doi.org/10.1002/jcc.27349

Official URL: Volltext

Abstract in another language

Abstract 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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: conformers; geometry prediction; graph neural network; machine learning
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf
Research Institutions > Central research institutes > Bayerisches Zentrum für Batterietechnik - BayBatt
Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning
Research Institutions
Research Institutions > Central research institutes
Result of work at the UBT: Yes
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 05 Oct 2024 21:00
Last Modified: 07 Oct 2024 09:38
URI: https://eref.uni-bayreuth.de/id/eprint/90574