Industry-Scale Orchestrated Federated Learning for Drug Discovery
Paper in proceeding, 2023

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.

Author

Martijn Oldenhof

KU Leuven

Gergely Ács

Budapest University of Technology and Economics

Balázs Pejó

Budapest University of Technology and Economics

Ansgar Schuffenhauer

Novartis International AG

Nicholas Holway

Novartis International AG

Noé Sturm

Novartis International AG

Arne Dieckmann

Bayer AG

Oliver Fortmeier

Bayer AG

Eric Boniface

Substra Foundation

Clément Mayer

Substra Foundation

Arnaud Gohier

Institut de Recherches Servier, Croissy-sur-Seine

Peter Schmidtke

Discngine

Ritsuya Niwayama

Institut de Recherches Servier, Croissy-sur-Seine

Dieter Kopecky

Boehringer Ingelheim

Lewis H. Mervin

AstraZeneca AB

Prakash Chandra Rathi

AstraZeneca AB

Lukas Friedrich

Merck KGaA

András Formanek

Budapest University of Technology and Economics

KU Leuven

Peter Antal

Budapest University of Technology and Economics

Jordon Rahaman

Amgen

Adam Zalewski

Amgen

Wouter Heyndrickx

Janssen

Ezron Oluoch

Kubermatic

Manuel Stößel

Kubermatic

Michal Vančo

Kubermatic

David Endico

Owkin

Fabien Gelus

Owkin

Thaïs de Boisfossé

Owkin

Adrien Darbier

Owkin

Ashley Nicollet

Owkin

Matthieu Blottière

Owkin

Maria Telenczuk

Owkin

Van Tien Nguyen

Owkin

Thibaud Martinez

Owkin

Camille Boillet

Owkin

Kelvin Moutet

Owkin

Alexandre Picosson

Owkin

Aurélien Gasser

Owkin

Inal Djafar

Owkin

Antoine Simon

Owkin

Ádám Arany

KU Leuven

Jaak Simm

KU Leuven

Yves Moreau

KU Leuven

Ola Engkvist

AstraZeneca AB

Chalmers, Computer Science and Engineering (Chalmers)

Other publications Research

Hugo Ceulemans

Janssen

Camille Marini

Owkin

Mathieu Galtier

Owkin

Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023

Vol. 37 15576-15584
9781577358800 (ISBN)

37th AAAI Conference on Artificial Intelligence, AAAI 2023
Washington, USA,

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