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Correction: MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks

Journal of Cheminformatics volume 15, Article number: 108 (2023) Cite this article

The Original Article was published on 11 October 2023


Correction: Journal of Cheminformatics (2023) 15:94 https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13321-023-00764-2


Following publication of the original article [1], we have been informed that Fig. 4 is missing, and instead of Fig. 4, Fig. 3 has been repeated as Fig. 4.


The original article [1] has been corrected.

Reference

  1. Jalali M, Wonanke ADD, Wöll C (2023) MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks. J Cheminform 15:94. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13321-023-00764-2

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Authors and Affiliations

  1. Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany

    Mehrdad Jalali, A. D. Dinga Wonanke & Christof Wöll

  2. Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany

    Mehrdad Jalali

Authors
  1. Mehrdad Jalali
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  2. A. D. Dinga Wonanke
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  3. Christof Wöll
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Correspondence to Mehrdad Jalali or Christof Wöll.

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Jalali, M., Wonanke, A.D.D. & Wöll, C. Correction: MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks. J Cheminform 15, 108 (2023). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13321-023-00774-0

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13321-023-00774-0

Journal of Cheminformatics

ISSN: 1758-2946

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