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Industry-Scale Orchestrated Federated Learning for Drug Discovery

Oldenhof, Martijn (author)
Katholieke Universiteit Leuven
Ács, Gergely (author)
Budapesti Muszaki es Gazdasagtudomanyi Egyetem,Budapest University of Technology and Economics
Pejó, Balázs (author)
Budapesti Muszaki es Gazdasagtudomanyi Egyetem,Budapest University of Technology and Economics
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Schuffenhauer, Ansgar (author)
Novartis International AG
Holway, Nicholas (author)
Novartis International AG
Sturm, Noé (author)
Novartis International AG
Dieckmann, Arne (author)
Bayer AG
Fortmeier, Oliver (author)
Bayer AG
Boniface, Eric (author)
Mayer, Clément (author)
Gohier, Arnaud (author)
Schmidtke, Peter (author)
Niwayama, Ritsuya (author)
Kopecky, Dieter (author)
Boehringer Ingelheim Pharma GmbH
Mervin, Lewis H. (author)
AstraZeneca AB
Rathi, Prakash Chandra (author)
AstraZeneca AB
Friedrich, Lukas (author)
Formanek, András (author)
Budapesti Muszaki es Gazdasagtudomanyi Egyetem,Budapest University of Technology and Economics,Katholieke Universiteit Leuven
Antal, Peter (author)
Budapesti Muszaki es Gazdasagtudomanyi Egyetem,Budapest University of Technology and Economics
Rahaman, Jordon (author)
Zalewski, Adam (author)
Heyndrickx, Wouter (author)
Oluoch, Ezron (author)
Stößel, Manuel (author)
Vančo, Michal (author)
Endico, David (author)
Gelus, Fabien (author)
de Boisfossé, Thaïs (author)
Darbier, Adrien (author)
Nicollet, Ashley (author)
Blottière, Matthieu (author)
Telenczuk, Maria (author)
Nguyen, Van Tien (author)
Martinez, Thibaud (author)
Boillet, Camille (author)
Moutet, Kelvin (author)
Picosson, Alexandre (author)
Gasser, Aurélien (author)
Djafar, Inal (author)
Simon, Antoine (author)
Arany, Ádám (author)
Katholieke Universiteit Leuven
Simm, Jaak (author)
Katholieke Universiteit Leuven
Moreau, Yves (author)
Katholieke Universiteit Leuven
Engkvist, Ola, 1967 (author)
AstraZeneca AB,Chalmers tekniska högskola,Chalmers University of Technology
Ceulemans, Hugo (author)
Marini, Camille (author)
Galtier, Mathieu (author)
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 (creator_code:org_t)
2023
2023
English.
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. ; 37, s. 15576-15584
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

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