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@ARTICLE{Nevolianis:1016101,
author = {Nevolianis, Thomas and Rittig, Jan Gerald and Mitsos,
Alexander and Leonhard, Kai},
title = {{M}ulti-fidelity graph neural networks for predicting
toluene/water partition coefficients},
journal = {Journal of cheminformatics},
volume = {17},
issn = {1758-2946},
address = {London},
publisher = {BioMed Central},
reportid = {RWTH-2025-06738},
pages = {123},
year = {2025},
cin = {416710 / 412110 / 080008},
ddc = {540},
cid = {$I:(DE-82)416710_20140620$ / $I:(DE-82)412110_20140620$ /
$I:(DE-82)080008_20150909$},
pnm = {OAPKF - Open-Access-Publikation mit Unterstützung der RWTH
Aachen University (021000-OAPKF) / DFG project
G:(GEPRIS)466417970 - Generatives graph-basiertes
maschinelles Lernen für das integrierte Design von
Molekülen und Prozessen (466417970) / DFG project
G:(GEPRIS)191948804 - SFB 985: Funktionelle Mikrogele und
Mikrogelsysteme (191948804) / SFB 985 B04 - Synthese von
Mikrogelen: Kinetik, Partikelbildung und Reaktordesign (B04)
(221473487) / DFG project G:(GEPRIS)441958259 - SPP 2331:
Maschinelles Lernen in der Verfahrenstechnik. Wissen trifft
auf Daten: Interpretierbarkeit, Extrapolation,
Verlässlichkeit, Vertrauen (441958259)},
pid = {G:(DE-82)021000-OAPKF / G:(GEPRIS)466417970 /
G:(GEPRIS)191948804 / G:(GEPRIS)221473487 /
G:(GEPRIS)441958259},
typ = {PUB:(DE-HGF)16},
doi = {10.1186/s13321-025-01057-6},
url = {https://publications.rwth-aachen.de/record/1016101},
}
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