Explainable graph neural networks for organic cages-Reference-Cited by-同舟云学术

Explainable graph neural networks for organic cages

Published:2022 Issue:2 Volume:1 Page:127-138
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Yuan Qi¹^ORCID,Szczypiński Filip T.¹^ORCID,Jelfs Kim E.¹^ORCID

Affiliation:

1. Department of Chemistry, Molecular Sciences Research Hub, White City Campus, Imperial College London, Wood Lane, London, UK

Abstract

We report the development of explainable Graph Neural Networks to predict shape persistence of organic cages. Integrated gradient analysis identifies collapse-inducing molecular fragments and helps chemists design more shape persistent structures.

Funder

Leverhulme Trust

Royal Society

H2020 European Research Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/DD/D1DD00039J

Reference31 articles.

1. Porous organic cages: soluble, modular and molecular pores

2. Covalent Organic Frameworks and Cage Compounds: Design and Applications of Polymeric and Discrete Organic Scaffolds

3. Functional Molecular Flasks: New Properties and Reactions within Discrete, Self-Assembled Hosts

4. Porous Organic Cages for Gas Chromatography Separations

5. Molecular shape sorting using molecular organic cages

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