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MachinE Learning Ledger Orchestration for Drug DiscoverY

Project information

Grant agreement ID: 831472

Status

Ongoing project

  • Start date

    1 June 2019

  • End date

    31 May 2022

Funded under:

H2020-EU.3.1.7.

  • Overall budget:

    € 18 410 465

  • EU contribution

    € 8 000 000

Coordinated by:

OWKIN FRANCE

France

Objective

MELLODDY will demonstrate how the pharmaceutical industry can better leverage its data assets to virtualize the Drug Discovery (DD) process with world-leading Machine Learning (ML) technologies as an answer to the ever-increasing challenges and stricter regulatory requirements it is facing. The lack of a tested, secure and privacy-preserving platform for federated machine learning that enables pharmaceutical partners to extract DD-relevant information from all types of, not only their own but even each other’s competitive data, without mutual disclosure of the chemistry and biology each partner has worked on, has previously held back such demonstration, to the detriment of patients in the EU and beyond.
MELLODDY’s ten pharmaceutical partners will enable this demonstration with an unprecedented volume of more than a billion highly private and competitive DD-relevant data points, and hundreds of Tbs of image data that annotate the biological effects of more than 10 million small molecules. The successful demonstration of the predictive benefits, i.e. increased predictive model performance and chemical applicability domain, of unlocking this data volume, while strictly preserving the privacy of all underlying data and the resulting predictive models, will shape best practices and translate into substantial efficiency gains in the DD process, and in the future, drug development. Finally, MELLODDY will prepare and exploit a service-for-fee vehicle to ensure the MELLODDY technologies are available to the rest of the pharmaceutical sector.

Field of Science

/natural sciences/computer and information sciences/artificial intelligence/machine learning

/social sciences/psychology/cognitive psychology/mental processes/learning

Programme(s)

H2020-EU.3.1.7. - Innovative Medicines Initiative 2 (IMI2)

Topic(s)

IMI2-2018-14-03 - Development of a platform for federated and privacy-preserving machine learning in support of drug discovery

Call for proposal

H2020-JTI-IMI2-2018-14-two-stage

See other projects for this call

Funding Scheme

IMI2-RIA - Research and Innovation action

Coordinator

OWKIN FRANCE

Address

75 Rue De Turbigo
75003 Paris

France

Activity type

Private for-profit entities (excluding Higher or Secondary Education Establishments)

EU Contribution

€ 2 773 635

Contact the organisation

Participants (16)

LOODSE GMBH

Germany

EU Contribution

€ 1 254 178

BUDAPESTI MUSZAKI ES GAZDASAGTUDOMANYI EGYETEM

Hungary

EU Contribution

€ 1 253 297

SUBSTRA

France

EU Contribution

€ 955 482

IKTOS

France

EU Contribution

€ 418 402

KATHOLIEKE UNIVERSITEIT LEUVEN

Belgium

EU Contribution

€ 1 345 006

JANSSEN PHARMACEUTICA NV

Belgium

ASTRAZENECA AB

Sweden

GLAXOSMITHKLINE RESEARCH AND DEVELOPMENT LTD.

United Kingdom

MERCK KOMMANDITGESELLSCHAFT AUF AKTIEN

Germany

BOEHRINGER INGELHEIM INTERNATIONALGMBH

Germany

BAYER AKTIENGESELLSCHAFT

Germany

NOVARTIS PHARMA AG

Switzerland

ASTELLAS PHARMA EUROPE BV

Netherlands

INSTITUT DE RECHERCHES SERVIER

France

AMGEN RESEARCH (MUNICH) GMBH

Germany

NVIDIA SWITZERLAND AG

Switzerland

Project information

Grant agreement ID: 831472

Status

Ongoing project

  • Start date

    1 June 2019

  • End date

    31 May 2022

Funded under:

H2020-EU.3.1.7.

  • Overall budget:

    € 18 410 465

  • EU contribution

    € 8 000 000

Coordinated by:

OWKIN FRANCE

France

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Last update: 17 January 2020

Record number: 223634