About
“The goal is to harness the collective knowledge of the consortium in a platform containing, amongst others, multi-task predictive machine learning algorithms incorporating an extended privacy management system, to identify the most effective compounds for drug development, while protecting the intellectual property rights of the consortium contributors.”
A new way of collaborating
“The MELLODDY project is a groundbreaking collaboration that has the potential to accelerate drug discovery and improve patient outcomes by enabling, for the first time, research to be conducted across the consortium’s decentralised and highly proprietary databases of annotated chemical libraries. This project allows the pharma partners for the first time to collaborate in their core competitive space, invigorating discovery efforts through efficiency gains.
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Background and objectives
Enabled by an ever-expanding arsenal of model systems, analysis methods, libraries of chemical compounds and other agents (like biologics), the amount of data generated during drug discovery programmes has never been greater, yet the biological complexity of many diseases still defies pharmaceutical treatment.
Hand in hand with rising regulatory expectations, this growing complexity has inflated the research intensity and associated cost of the average discovery project. It is, therefore, imperative that the learnings from these data investments are maximised to enable efficient future research.
This could be empowered by the big data analysis and machine learning approaches that are currently driving the digital transformation across all industries.
MELLODDY aims to transcend most constraints of current Machine Learnings (ML) practices in drug discovery, by demonstration of the working hypothesis:
Multi-task ML across data types and partners boosts the predictive performance and chemical applicability domain of drug discovery-relevant models, without unacceptable leaks of private information.
We attempt to accomplish this through the creation of a flexible, scalable and secure framework for federated and privacy-preserving ML that can train and evaluate drug discovery-relevant predictive models;
and an audit, stress-test and evaluation of the platform on an unprecedented volume of drug discovery-relevant industrial and selected public data in three yearly iterations.
Find out more about how the MELLODDY project plans to accomplish these goals by clicking below.