  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{\deg}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.
