Skip to main content
Download PDF
Download ePub

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://doi.org/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://doi.org/10.1186/s13321-023-00764-2

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. D. Dinga Wonanke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christof Wöll
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mehrdad Jalali or Christof Wöll.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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://doi.org/10.1186/s13321-023-00774-0

Download citation

  • Published:

  • DOI: https://doi.org/10.1186/s13321-023-00774-0

Journal of Cheminformatics

ISSN: 1758-2946

Contact us