S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties

Adem R. N.  Aouichaoui; Alessandro Cogliati; Jens Abildskov; Gürkan Sin

doi:10.1016/B978-0-443-15274-0.50091-3

S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties

Adem R. N. Aouichaoui, Alessandro Cogliati, Jens Abildskov, Gürkan Sin^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Original language	English
Title of host publication	Proceedings of the 33rd European Symposium on Computer Aided Process Engineering
Editors	Antonis Kokossis, Michael C. Georgiadis, Efstratios N. Pistikopoulos
Volume	52
Publisher	Elsevier
Publication date	2023
Pages	575-581
ISBN (Print)	978-0-443-23553-5, 978-0-443-15274-0
DOIs	https://doi.org/10.1016/B978-0-443-15274-0.50091-3
Publication status	Published - 2023
Event	33rd European Symposium on Computer Aided Process Engineering - Athens, Greece Duration: 18 Jun 2023 → 21 Jun 2023

Conference

Conference	33rd European Symposium on Computer Aided Process Engineering
Country/Territory	Greece
City	Athens
Period	18/06/2023 → 21/06/2023

Series	Computer Aided Chemical Engineering
Volume	52
ISSN	1570-7946

Keywords

Graph neural networks
Thermophysical properties
Molecules
QSPR

Access to Document

10.1016/B978-0-443-15274-0.50091-3

OpenUrl availability

Full text

Cite this

Aouichaoui, A. R. N., Cogliati, A., Abildskov, J., & Sin, G. (2023). S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties. In A. Kokossis, M. C. Georgiadis, & E. N. Pistikopoulos (Eds.), Proceedings of the 33rd European Symposium on Computer Aided Process Engineering (Vol. 52, pp. 575-581). Elsevier. https://doi.org/10.1016/B978-0-443-15274-0.50091-3

Aouichaoui, Adem R. N. ; Cogliati, Alessandro ; Abildskov, Jens et al. / S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties. Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. editor / Antonis Kokossis ; Michael C. Georgiadis ; Efstratios N. Pistikopoulos. Vol. 52 Elsevier, 2023. pp. 575-581 (Computer Aided Chemical Engineering, Vol. 52).

@inproceedings{ed821735b6664542be53c58563301ffc,

title = "S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties",

keywords = "Graph neural networks, Thermophysical properties, Molecules, QSPR",

author = "Aouichaoui, {Adem R. N.} and Alessandro Cogliati and Jens Abildskov and G{\"u}rkan Sin",

year = "2023",

doi = "10.1016/B978-0-443-15274-0.50091-3",

language = "English",

isbn = "978-0-443-23553-5",

volume = "52",

series = "Computer Aided Chemical Engineering",

publisher = "Elsevier",

pages = "575--581",

editor = "Kokossis, {Antonis } and {C. Georgiadis}, {Michael } and {N. Pistikopoulos}, Efstratios",

booktitle = "Proceedings of the 33rd European Symposium on Computer Aided Process Engineering",

address = "United Kingdom",

note = "33rd European Symposium on Computer Aided Process Engineering, ESCAPE33 ; Conference date: 18-06-2023 Through 21-06-2023",

}

Aouichaoui, ARN, Cogliati, A, Abildskov, J & Sin, G 2023, S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties. in A Kokossis, M C. Georgiadis & E N. Pistikopoulos (eds), Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. vol. 52, Elsevier, Computer Aided Chemical Engineering, vol. 52, pp. 575-581, 33rd European Symposium on Computer Aided Process Engineering, Athens, Greece, 18/06/2023. https://doi.org/10.1016/B978-0-443-15274-0.50091-3

S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties. / Aouichaoui, Adem R. N. ; Cogliati, Alessandro; Abildskov, Jens et al.
Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. ed. / Antonis Kokossis; Michael C. Georgiadis; Efstratios N. Pistikopoulos. Vol. 52 Elsevier, 2023. p. 575-581 (Computer Aided Chemical Engineering, Vol. 52).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties

AU - Aouichaoui, Adem R. N.

AU - Cogliati, Alessandro

AU - Abildskov, Jens

AU - Sin, Gürkan

PY - 2023

Y1 - 2023

KW - Graph neural networks

KW - Thermophysical properties

KW - Molecules

KW - QSPR

U2 - 10.1016/B978-0-443-15274-0.50091-3

DO - 10.1016/B978-0-443-15274-0.50091-3

M3 - Article in proceedings

SN - 978-0-443-23553-5

SN - 978-0-443-15274-0

VL - 52

T3 - Computer Aided Chemical Engineering

SP - 575

EP - 581

BT - Proceedings of the 33rd European Symposium on Computer Aided Process Engineering

A2 - Kokossis, Antonis

A2 - C. Georgiadis, Michael

A2 - N. Pistikopoulos, Efstratios

PB - Elsevier

T2 - 33rd European Symposium on Computer Aided Process Engineering

Y2 - 18 June 2023 through 21 June 2023

ER -

Aouichaoui ARN, Cogliati A, Abildskov J , Sin G. S-GNN: State-Dependent Graph Neural Networks for Functional Molecular Properties. In Kokossis A, C. Georgiadis M, N. Pistikopoulos E, editors, Proceedings of the 33rd European Symposium on Computer Aided Process Engineering. Vol. 52. Elsevier. 2023. p. 575-581. (Computer Aided Chemical Engineering, Vol. 52). doi: 10.1016/B978-0-443-15274-0.50091-3