Industry-Scale Orchestrated Federated Learning for Drug Discovery

Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; Ritsuya Niwayama; Dieter Kopecky; Lewis H. Mervin; Prakash Chandra Rathi; Lukas Friedrich; András Formanek; Peter Antal; Jordon Rahaman; Adam Zalewski; Wouter Heyndrickx; Ezron Oluoch; Manuel Stößel; Michal Vančo; David Endico; Fabien Gelus; Thaïs de Boisfossé; Adrien Darbier; Ashley Nicollet; Matthieu Blottière; Maria Telenczuk; Van Tien Nguyen; Thibaud Martinez; Camille Boillet; Kelvin Moutet; Alexandre Picosson; Aurélien Gasser; Inal Djafar; Antoine Simon; Ádám Arany; Jaak Simm; Yves Moreau; Ola Engkvist; Hugo Ceulemans; Camille Marini; Mathieu Galtier

Industry-Scale Orchestrated Federated Learning for Drug Discovery
Paper i 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.

Författare

Martijn Oldenhof

KU Leuven

Gergely Ács

Budapesti Muszaki es Gazdasagtudomanyi Egyetem

Balázs Pejó

Budapesti Muszaki es Gazdasagtudomanyi Egyetem

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

Budapesti Muszaki es Gazdasagtudomanyi Egyetem

KU Leuven

Peter Antal

Budapesti Muszaki es Gazdasagtudomanyi Egyetem

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, Data- och informationsteknik

Forskning Andra publikationer

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,

Ämneskategorier

Systemvetenskap

Datavetenskap (datalogi)

Mer information

Senast uppdaterat

2024-01-03

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

Författare

Martijn Oldenhof

Gergely Ács

Balázs Pejó

Ansgar Schuffenhauer

Nicholas Holway

Noé Sturm

Arne Dieckmann

Oliver Fortmeier

Eric Boniface

Clément Mayer

Arnaud Gohier

Peter Schmidtke

Ritsuya Niwayama

Dieter Kopecky

Lewis H. Mervin

Prakash Chandra Rathi

Lukas Friedrich

András Formanek

Peter Antal

Jordon Rahaman

Adam Zalewski

Wouter Heyndrickx

Ezron Oluoch

Manuel Stößel

Michal Vančo

David Endico

Fabien Gelus

Thaïs de Boisfossé

Adrien Darbier

Ashley Nicollet

Matthieu Blottière

Maria Telenczuk

Van Tien Nguyen

Thibaud Martinez

Camille Boillet

Kelvin Moutet

Alexandre Picosson

Aurélien Gasser

Inal Djafar

Antoine Simon

Ádám Arany

Jaak Simm

Yves Moreau

Ola Engkvist

Hugo Ceulemans

Camille Marini

Mathieu Galtier

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

Ämneskategorier

Mer information

Senast uppdaterat

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