MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.
Heyndrickx W, Mervin L, Morawietz T, Sturm N, Friedrich L, Zalewski A, Pentina A, Humbeck L, Oldenhof M, Niwayama R, Schmidtke P, Fechner N, Simm J, Arany A, Drizard N, Jabal R, Afanasyeva A, Loeb R, Verma S, Harnqvist S, Holmes M, Pejo B, Telenczuk M, Holway N, Dieckmann A, Rieke N, Zumsande F, Clevert DA, Krug M, Luscombe C, Green D, Ertl P, Antal P, Marcus D, Do Huu N, Fuji H, Pickett S, Acs G, Boniface E, Beck B, Sun Y, Gohier A, Rippmann F, Engkvist O, Göller AH, Moreau Y, Galtier MN, Schuffenhauer A, Ceulemans H.
Heyndrickx W, et al. Among authors: krug m.
J Chem Inf Model. 2024 Apr 8;64(7):2331-2344. doi: 10.1021/acs.jcim.3c00799. Epub 2023 Aug 29.
J Chem Inf Model. 2024.
PMID: 37642660
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