Abstract

A little over two years ago, the MELLODDY consortium launched a bold, privacy-preserving federated learning (FL) research experiment. Over the course of three years, its ten pharmaceutical partners are involving the largest part of their data warehouses and engaging in a series of three federated runs deploying the technology developed in collaboration with its technology and academic partners. MELLODDY tests the hypothesis that federated ML approaches can overcome data sharing challenges and privacy concerns for competitive partners with a mutual interest in building predictive models. MELLODDY’s first year results presented a technical demonstration of advancements in AI with the successful operation of a rigorously audited platform comprising three indispensable layers: cloud infrastructure, application, and algorithm. The project has now completed its second cross-partner run across more than 100,000 machine learning tasks representing more than 40,000 concentration response assays using an improved and re-audited platform. The run provides early evidence that federated learning indeed boosts the predictive performance and chemical applicability of models used to inform drug discovery programs.