The new research consortium seeks to accelerate drug discovery using machine learning to unlock maximum potential of pharma industry data. The project aims to leverage the world’s largest collection of small molecules with known biochemical or cellular activity to enable more accurate predictive models and increase efficiencies in drug discovery.
A new consortium of pharmaceutical, technology and academic partners has announced the launch of the “MELLODDY” (Machine Learning Ledger Orchestration for Drug Discovery) project which aims, for the first time, to use machine learning methods on the chemical libraries of 10 pharma companies and to develop a platform creating more accurate models to predict which compounds could be promising in the later stages of drug discovery and development.
The project, which demonstrates a new model of collaboration between traditional competitors in drug discovery, involves an unprecedented volume of competitive data. Through MELLODDY, a sophisticated platform is being developed that addresses at the same time the need for security and privacy preservation while allowing for enough information exchange to boost predictive performance.
The project involves 17 partners from across Europe and receives funding from the Innovative Medicines Initiative (IMI) as a public-private partnership. Janssen Pharmaceutica NV, one of the Pharmaceutical Companies of Johnson & Johnson, is the pharmaceutical industry lead of the project with coordination provided by Owkin. The project will operate for three years, concluding in June 2022, with an estimated budget of €18,4 million.
“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,” said Hugo Ceulemans, MELLODDY Project Leader and Scientific Director, Discovery Data Sciences at Janssen Pharmaceutica NV. “This project allows the pharma partners for the first time to collaborate in their core competitive space, invigorating discovery efforts through efficiency gains.”
“The MELLODDY consortium will use Owkin’s block-chain architecture technology to extract insight from multiple datasets without having to first pool the data,” said Mathieu Galtier, Project Coordinator, Owkin. “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. We are excited to be part of this bold, federated learning research revolution.”
About the MELLODDY Project
MELLODDY aims to train machine learning models across multi-partner datasets while ensuring privacy preservation of both the data and the models by developing a platform using federated learning. The MELLODDY platform uses Amazon Web Services technologies in order to execute Machine Learning algorithms from academic partners on a large scale. The data never leaves the owner’s infrastructure and only non-sensitive models are exchanged. A central dispatcher allows each partner to share a common model to be consolidated collectively. To provide full traceability of the operations, the platform is based on a private blockchain. This means that a ledger will be distributed across all contributing pharma partners in such a way that there is no central authority. The platform guarantees by design that partners keep control and visibility over their own private data. Since there is no central authority, any communication between the dispatcher and a ledger needs to be approved by all partners before one can proceed. Like a bank statement, the ledger holds a log of all activities and can be requested after a federated run. The MELLODDY platform is designed to prevent the leaking of proprietary information from one data set to another or through one model to another while at the same time boosting the predictive performance and applicability domain of the models by leveraging all available data.
The MELLODDY consortium consists of 17 partners:
- 10 pharmaceutical companies: Amgen, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, GSK, Janssen Pharmaceutica NV, Merck KgaA, Novartis, and Institut de Recherches Servier
- 2 academic universities: KU Leuven, Budapesti Muszaki es Gazdasagtudomanyi Egyetem
- 4 subject matter experts: Owkin, Substra Foundation, Kubermatic, Iktos
- 1 large AI computing company: NVIDIA
About the Innovative Medicines Initiative
The Innovative Medicines Initiative (IMI) is a partnership between the European Union and the European pharmaceutical industry, represented by the European Federation of Pharmaceutical Industries and Associations (EFPIA). It is working to improve health by speeding up the development of, and patient access to, the next generation of medicines, particularly in areas where there is an unmet medical or social need. More info on IMI: www.imi.europa.eu
Contact
MELLODDY Communication: Kim ROTONDO – Janssen (KRotondo@its.jnj.com) ; Michèle BENTATA - OWKIN (michele.bentata@owkin.com)
Acknowledgement
This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 831472. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA Companies.
Disclaimer
This communication reflects the views of the authors and neither IMI nor the European Union, EFPIA or any Associated Partners are liable for any use that may be made of the information contained herein.
