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"MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks ..."

Wouter Heyndrickx et al. (2024)

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DOI: 10.1021/ACS.JCIM.3C00799

access: closed

type: Journal Article

metadata version: 2024-05-07

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  dblp key:
  - journals/jcisd/HeyndrickxMMSFZPHONSFSADJALVHHPTH24
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  persistent URL:
  - https://dblp.org/rec/journals/jcisd/HeyndrickxMMSFZPHONSFSADJALVHHPTH24
Wouter Heyndrickx, Lewis H. Mervin, Tobias Morawietz, Noé Sturm, Lukas Friedrich, Adam Zalewski, Anastasia Pentina, Lina Humbeck, Martijn Oldenhof, Ritsuya Niwayama, Peter Schmidtke, Nikolas Fechner, Jaak Simm, Adam Arany, Nicolas Drizard, Rama Jabal, Arina Afanasyeva, Regis Loeb, Shlok Verma, Simon Harnqvist, Matthew Holmes, Balazs Pejo, Maria Telenczuk, Nicholas Holway, Arne Dieckmann, Nicola Rieke, Friederike Zumsande, Djork-Arné Clevert, Michael Krug, Christopher N. Luscombe, Darren V. S. Green, Peter Ertl, Peter Antal, David Marcus, Nicolas Do Huu, Hideyoshi Fuji, Stephen D. Pickett, Gergely Ács, Eric Boniface, Bernd Beck, Yax Sun, Arnaud Gohier, Friedrich Rippmann, Ola Engkvist, Andreas H. Göller, Yves Moreau, Mathieu N. Galtier, Ansgar Schuffenhauer, Hugo Ceulemans:
MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. J. Chem. Inf. Model. 64(7): 2331-2344 (2024)

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