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. To the best of our knowledge, 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.
翻译:为了将联合会学习应用于药物发现,我们在欧洲创新医药倡议项目MELLODDY(MELLODDY)项目MELLODDY(Grant n=deg}831472)范围内开发了一个新平台,该项目由10家制药公司、学术研究实验室、大型工业公司和新开办企业组成。据我们所知,MELLODDY平台是第一个工业规模的平台,目的是在不分享个别伙伴的保密数据集的情况下创建全球联合药物发现模式。该联合模型在该平台上接受培训,将所有贡献伙伴的梯度在每次培训迭代后的安全方式以加密方式汇总在一起。该平台部署在一个亚马孙网络服务(AWS)多账户结构上,运行私人子网中的库伯涅斯集群。从组织上看,不同伙伴的作用被编为平台上的不同权利和许可,并以分散方式加以管理。MELLODDDY平台产生了新的科学发现,在一份配套文件中作了描述。