June 26, 2023

Industry-scale orchestrated federated learning for drug discovery


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.

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