Research & Innovation - Participant Portal


TOPIC : Development of a platform for federated and privacy-preserving machine learning in support of drug discovery

Topic identifier: IMI2-2018-14-03
Publication date: 15 March 2018

Types of action: IMI2-RIA Research and Innovation action
Opening date:
15 March 2018
2nd stage Deadline:
14 June 2018 17:00:00
11 December 2018 17:00:00

Time Zone : (Brussels time)
  Horizon 2020 H2020 website
Pillar: Societal Challenges
Work Programme Year: H2020-JTI-IMI-2018
Work Programme Part: H2020-JTI-IMI-2018
Topic Description
Specific Challenge:

For full details of the topic, please refer to the call text

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.


For full details of the topic, please refer to the call text

The delivery of a federated and privacy-preserving machine learning platform, initially validated on publicly accessible data, that is demonstrably safe enough (privacy-preserving in the face of legitimate and illegitimate (attempted) access and use) and scalable enough to be deployed to a significant representation of private data in the actual preclinical data warehouses of the participating major pharmaceutical companies in yearly evaluation runs. This effort will be mainly driven by the applicant consortium and enabled by the EFPIA partners.

Expected Impact:

For full details of the topic, please refer to the call text

The in silico predictions from the platform developed within the project will increasingly replace the costly and time-consuming in vitro testing, resulting in cost and time savings on compound synthesis and measurement in assays and preclinical studies, and therefore increase the efficiency of pharmaceutical discovery research. Although out of the direct scope of the present topic, the application of similar concepts to clinical data to enable faster recruitment of more targeted patients holds the longer-term promise of reducing costs of development.

The concepts developed within the project will be generic and will apply not only to the pharmaceutical discovery and clinical development setting, but also to other clinical applications, including real-world evidence analysis. Beyond the health area, they will prove relevant to multiple alternative industrial and other commercial or non-commercial settings where parties are interested in different predictive models that benefit from indirect access to the same volumes of private data. By providing data owners with the confidence that their data and the corresponding predictive models will remain private, this project will facilitate access to much larger data sets and therefore improve performance over that of conventional machine learning approaches.

For knowledge and ICT partners, federated learning presents a line of research and product development beyond that of data federation.

Applicants should indicate how their proposal will impact on the competitiveness and industrial leadership of Europe by, for example engaging suitable SMEs.

Topic conditions and documents

Please read carefully all provisions below before the preparation of your application.

The IMI2 JU 14th Call for proposals topics text as well as the Call Conditions are available here

The budget breakdown for this Call is given at the end of the Call topics text, in the Call Conditions section, as well as the following information : 


1.   Eligible countries: described in article 10(2) of the Rules for participation in Horizon 2020 and in article 1 of the Commission Delegated Regulation related to IMI JU.


2.   Eligibility and admissibility conditions: described in the IMI2 Manual for evaluation, submission and grant award. See also the Commission Delegated Regulation related to IMI JU.


Proposal page limits and layout: Please refer to Part B of the proposal template in the submission tool below.


3.   Evaluation:
Submission and evaluation process, including evaluation criteria and procedure, scoring and threshold are described in the IMI2 Manual for submission, evaluation and grant award. See also the proposal templates for your specific action in section 5, below.


4.   Indicative time for evaluation and grant agreement:
Notification of outcomes of stage 1 evaluations: maximum 5 months from deadline for submitting proposals.
Notification of outcomes of stage 2 evaluations: maximum 5 months from deadline for submitting full proposals.

Signature of grant agreements: maximum 3 months from the date of informing successful applicants.


5.   Proposal templates, evaluation forms and model grant agreements (MGA):

IMI2 Research and Innovation Action (IMI2-RIA) and Innovation Action (IMI2-IA):

Proposal templates are available after entering the submission tool

Standard evaluation form

IMI2 Model Grant Agreement

Clinical trial template – the Clinical Trial template is compulsory at stage 2 only !


6.   Open access must be granted to all scientific publications resulting from Horizon 2020 actions.

Where relevant, proposals should also provide information on how the participants will manage the research data generated and/or collected during the project, such as details on what types of data the project will generate, whether and how this data will be exploited or made accessible for verification and re-use, and how it will be curated and preserved.

Open access to research data
The Open Research Data Pilot has been extended to cover all Horizon 2020 topics for which the submission is opened on 26 July 2016 or later. Projects funded under this topic will therefore by default provide open access to the research data they generate, except if they decide to opt-out under the conditions described in Annex L of the H2020 main Work Programme. Projects can opt-out at any stage, that is both before and after the grant signature.

Note that the evaluation phase proposals will not be evaluated more favourably because they plan to open or share their data, and will not be penalised for opting out.

Open research data sharing applies to the data needed to validate the results presented in scientific publications. Additionally, projects can choose to make other data available open access and need to describe their approach in a Data Management Plan.

Projects need to create a Data Management Plan (DMP), except if they opt-out of making their research data open access. A first version of the DMP must be provided as an early deliverable within six months of the project and should be updated during the project as appropriate. The Commission already provides guidance documents, including a template for DMPs. See the Online Manual.

Eligibility of costs: costs related to data management and data sharing are eligible for reimbursement during the project duration.

The legal requirements for projects participating in this pilot are in the article 29.3 of the Model Grant Agreement.


Members of consortium are required to conclude a consortium agreement prior to the signature of the grant agreement.

7. Additional documents:

Summary of the most relevant provisions for participating in IMI2 actions

IMI2 Amended Annual Work Plan 2018

IMI2 JU Annotated Model Grant Agreement

IMI2 Regulators Guidance tool for researchers

IMI JU derogation to H2020 Rules for Participation  

Horizon 2020 Rules for Participation 

Horizon 2020 Regulation of Establishment

Horizon 2020 Specific Programme


Additional documents

  • IMI2 Call 14 stage 1 - Flash Call Info Report en

Submission Service

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