List of innovations by the CoEs, spotted by the EU innovation radar

The EU Innovation Radar aims to identify high-potential innovations and innovators. It is an important source of actionable intelligence on innovations emerging from research and innovation projects funded through European Union programmes. 
These are the innovations from the HPC Centres of Excellence as spotted by the EU innovation radar:

Title: GROMACS, a versatile package to perform molecular dynamics
Market maturity: Exploring
Project: BioExcel
Innovation Topic: Excellent Science


Title: Urgent Computing services for the impact assessment in the immediate aftermath of an earthquake
Market maturity: Tech Ready
Market creation potential: High
Project: ChEESE
Innovation Topic: Excellent Science

esiways logo_type_grey_left copy

Table: New coupled earth system model
Market maturity: Tech Ready
Project: ESiWACE
Innovation Topic: Excellent Science


Title: In-Situ Analysis of CFD Simulations
Market maturity: Tech Ready
Market creation potential: High
Project: Excellerat
Innovation Topic: Excellent Science

Title: Interactive in situ visualization in VR
Market maturity: Tech Ready
Market creation potential: High
Project: Excellerat
Innovation Topic: Excellent Science

Title: Machine Learning Methods for Computational Fluid Dynamics (CFD) Data
Market maturity: Tech Ready
Market creation potential: Noteworthy
Project: Excellerat
Innovation Topic: Excellent Science


Title: Quantum Simulation as a Service
Market maturity: Exploring
Market creation potential: Noteworthy
Project: MaX
Innovation Topic: Excellent Science


In silico trials for effects of COVID-19 drugs on heart

 A Use Case by

Short description

Treatment of COVID-19 identified two potentially effective drugs, azithromycin and hydroxychloroquine, however, these were known to have proarrhythmic effects (disruption of the rhythm of the heart) especially in those with underlying heart conditions. Little was known about the effect of these drugs on the arrhythmic risk to patients who receive it as a COVID-19 therapy or the effect on the heart of using the drugs in combination.

Barcelona Supercomputing Centre (BSC) conducted research in collaboration with ELEM Biotech (ELEM) to assess the impact of the drugs on the human heart, to provide guidance for clinicians on dosages and risks. Alya Red is used in their research, a multiscale model developed by BSC which performs cardiac electro-mechanics simulations, from tissue to organ level. This type of computational modelling can provide a unique tool to develop models for the function of the heart and assess the cardiotoxicity of drugs to a high level of detail, without the need for lengthy and expensive clinical research.

The state-of-the-art models being employed and developed will also enable them to study the varied effects between groups of individuals and look at the comorbidities for such things as gender, ischemia, metabolite imbalances, and structural diseases.

Results & Achievements

Early results from the BSC/ELEM models have already identified how a conduction block may occur as an effect from action potential duration lengthening from the use of chloroquine. The arrhythmic risk was assessed during a stress test on the population (at elevated heart around 150 bpm) and with a variety of doses of the medication. The results obtained on the human population (QTc interval duration and arrhythmic risk) were compared to clinical trials performed on COVID-19 (SARS-CoV-2) patients. An in-silico cardiac population was capable of providing remarkably similar percentages of subjects at risk, as were identified in the clinic. Furthermore, the virtual population allowed the identification of phenotypes at risk of drug-induced arrhythmias.


  • assess the impact of the drugs on a human heart population
  • provide guidance for clinicians on dosages and risks
  • study the varied effects between groups of individuals.
  • analyse the effect of the antimalarial drugs on a range of simulated human hearts with a variety of comorbidities which may be present in an infected population.


Collaborating Institutions

Visible Heart Laboratories
University of Minnesota

CT2S - Computer Tomography to Strength

Digital Twin Application for patient treatment & BoneStrength: In Silico Trials solution

 A Use Case by

Short description

The CT2S (Computer Tomography to Strength) application was designed to predict the risk of hip fracture at the time of a CT scan, creating a patient-specific finite-element model of the femur. The CT2S pipeline comprises femur geometry segmentation, high-quality 3D mesh generation, element-wise material property mapping, and eventually finite-element model simulation. By using a disease progression model, this is now being extended to assess the Absolute Risk of Fracture up to 10 years (ARF10). In parallel with this, a solution known as BoneStrength is being developed (based on algorithms in CT2S and ARF10) to build an in silico trial application for bone-related drugs and treatments. Currently, the BoneStrength pipeline is being used on a cohort of 1000 digital patients to simulate the effect of an osteoporosis drug.

Results & Achievements

  • M. Taylor, M. Viceconti, P. Bhattacharya, X. Li 2021. “Finite element analysis informed var-iable selection for femoral fracture risk predic-tion”, Journal of the Mechanical Behavior of Biomedical Materials, in press.
  • C. Winsor, X. Li, M. Qasim, C.R. Henak, P.J. Pickhardt, H. Ploeg, M. Viceconti 2021. “Eval-uation of patient tissue selection methods for deriving equivalent density calibration for femo-ral bone quantitative CT analyses”, Bone, 143, 115759.
  • Z. Altai, E. Montefiori, B. van Veen, M. A. Paggiosi, E. V. McCloskey, M. Viceconti, C. Mazzà, X. Li 2021. “Femoral neck strain pre-diction during level walking using a combined musculoskeletal and finite element model ap-proach”, PLoS ONE, 68.

The codes are currently running on three HPC clusters, namely, ShARC (Tier-3, hosted by University of Sheffield, England), Cartesius (Tier-1, hosted by SURF, The Netherlands), and Galileo (Tier-1, hosted by CINECA, Italy).


  • To develop an efficient digital twin solution for the estimation of long bone fracture risk in elderly patients (CT2S solution)
  • To not only be able to evaluate the bone strength at present, but also predict the changes in the next 10 years (ARF10 solution)
  • To extend the digital twin solution to predict other types of fracture, such as the vertebral
  • To enable the running of large scale in silico clinical trials (BoneStrength solution, with a current target for bone treatments)
  • To port these solutions on multiple HPC platforms across Europe and solve potential scalability issues



Use Case Owner

Marco Viceconti
University of Bologna

Collaborating Institutions

University of Bologna
University of Sheffield

Strong scaling performance for human scale blood-flow modelling

 A Use Case by

Short description

To get the best out of future exascale machines, current codes must be able to demonstrate good scaling performance on current machines. The HemeLB code has demonstrated such characteristics to full machine scale on SuperMUC-NG. However, this performance must be aligned with the ability to solve practical problems. We have developed and deployed a self-coupled version of HemeLB that allows us to simultaneously study flows in linked arterial and venous vasculatures in 3D. In this use case, we look towards the application of flow in an arteriovenous fistula. A fistula is created in patients with kidney failure to increase flow in a chosen vein in order to provide an access point for dialysis treatment.

Results & Achievements

In collaboration with POP CoE, we were able to demonstrate HemeLB’s capacity for strong scaling behaviour up to the full production partition of SuperMUC-NG (>300,000 cores) whilst using a non-trivial vascular domain. This highlighted several challenges of running simulations at scale and also identified avenues for us to improve the performance of the HemeLB code. We’ve also run self-coupled simulations on personalised 3D arteries and veins of the left forearm with and without an arteriovenous fistula being created. The initial flow from our modified model showed good agreement with that seen in a clinical study.


The first objective of this use case was to demonstrate and assess the strong scaling performance of HemeLB to the largest core counts possible. This was to enable us to evaluate current performance and identify improvements for future exascale machines. The second main objective was to demonstrate the ability of HemeLB to utilise this performance to study flows on human-scale vasculatures. The combination of both aspects will be essential to enabling the creation of a virtual human able to simulate the specific physiology of an individual for diagnostic purposes and evaluation of potential treatments.


Use Case Owner

Peter Coveney
University College London

Collaborating Institutions

POP CoE (Julich Supercomputing Centre)

(c) Fusion Medical Animation on Unsplash

How EU projects work on supercomputing applications to help contain the corona virus pandemic

The Centres of Excellence in high-performance computing are working to improve supercomputing applications in many different areas: from life sciences and medicine to materials design, from weather and climate research to global system science. A hot topic that affects many of the above-mentioned areas is, of course, the fight against the corona virus pandemic.

There are rather obvious challenges for those EU projects that are developing HPC applications for simulations in medicine or in the life sciences, like CompBioMed (Biomedicine) BioExcel (Biomolecular Research), and PerMedCoE (Personalized Medicine). But also other projects from scientific areas, that you would, at first sight, not directly relate to research on the pandemic, are developing and using appropriate applications to model the virus and its spread, and support policy makers with computing-heavy simulations. For example, did you know that researchers can simulate the possible spread of the virus on a local level, taking into account measures like closing shops or quarantining residents?

This article gives an overview over the various ways in which EU projects are using supercomputing applications to tackle and support the global challenge of containing the pandemic.

Simulations for better and faster drug development

CompBioMed is an EU-funded project working on applications for computational biomedicine. It is part of a vast international consortium across Europe and USA working on urgent coronavirus research. “Modelling and simulation is being used in all aspects of medical and strategic actions in our fight against coronavirus. In our case, it is being harnessed to narrow down drug targets from billions of candidate molecules to a handful that can be clinically trialled”, says Peter Coveney from University College London (UCL) who is heading CompBioMed’s efforts in this collaboration. The goal is to accelerate the development of antiviral drugs by modelling proteins that play critical roles in the virus life cycle in order to identify promising drug targets.

Secondly, for drug candidates already being used and trialled, the CompBioMed scientists are modelling and analysing the toxic effects that these drugs may have on the heart, using supercomputing resources required to run simulations on such scales.  The goal is to assess the drug dosage and potential interactions between drugs to provide guidance for their use in the clinic.

Finally, the project partners analysed a model used to inform the UK Government’s response to the pandemic. It has been found to contain a large degree of uncertainty in its predictions, leading it to seriously underestimate the first wave. “Epidemiological modelling has been and continues to be used for policy-making by governments to determine healthcare interventions”, says Coveney. “We have investigated the reliability of such models using HPC methods required to truly understand the uncertainty and sensitivity of these models.” As a conclusion, a better public understanding of the inherent uncertainty of models predicting COVID-19 mortality rates is necessary, saying they should be regarded as “probabilistic” rather than being relied upon to produce a particular and specific outcome.

Image of SuperMUC-NG, supercomputer at Leibniz Supercomputing Centre of the Bavarian Academy of Sciences. (c)MMM/LRZ
Image of SuperMUC-NG, supercomputer at Leibniz Supercomputing Centre of the Bavarian Academy of Sciences, consortium member in the CompBioMed project. (c) MMM/LRZ

BioExcel is an EU-funded project developing some of the most popular applications for modelling and simulations of biomolecular systems. Along with code development, the project builds training programmes to address competence gaps in extreme-scale scientific computing for beginners, advanced users and system maintainers.

When COVID-19 struck, BioExcel launched a series of actions to support the community on SARS-CoV-2 research, with an extensive focus on facilitating collaborations, user support, and providing access to HPC resources at partner centers. BioExcel partnered with Molecular Sciences Software Institute to establish the COVID-19 Molecular Structure and Therapeutics Hub to allow researchers to deposit their data and review other group’s submissions as well.

During this period, there was an urgent demand for diagnostics and sharing of data for COVID-19 applications had become vital more than ever. A dedicated BioExcel-CV19 web-server interface was launched to provide access to study molecules involved in the COVID-19 disease. This allowed the project to be a part of open access initiative promoted by the scientific community to make research accessible.

Recently, BioExcel endorsed the EU manifesto for COVID-19 Research launched by European Commission as part of their response to the coronavirus outbreak.

Modelling the electronic structure of the protease

MaX (MAterials design at the eXascale) is a European Centre of Excellence aiming at materials modelling, simulations, discovery and design on the exascale supercomputing architectures.

Though the main interest of the MaX flagship codes is then centered on materials science, the CoE is participating in the fight against SARS-CoV-2. Given the critical pandemic situation that the world is currently facing, an unprecedented effort is being devoted to the study of SARS-CoV-2 by researchers from different scientific communities and groups worldwide. From the biomolecular standpoint, particular focus is being devoted to the main protease, as well as to the spike protein. As such, it is an important potential antiviral drug target: if its function is inhibited, the virus remains immature and non-infectious. Using fragment-based screening, researchers have identified a number of small compounds that bind to the active site of the protease and can be used as a starting point for the development of protease inhibitors.

Sars-Cov-2 main protease monomer, in green, with the N3 3-mer peptide inhibitor bound in the enzyme’s active site.(from PDB crystal structure 6lu7). Structure like this ones can be simulated with a full DFT calculation and automatically decomposed into fragments whose interaction network can be characterized and analyzed.

Among other quantities, MaX researchers now have the possibility to model the electronic structure of the protease in contact with a potential docked inhibitor, and provide new insights on the interactions between them by selecting specific amino-acids that are involved in the interaction and characterizing their polarities. This new approach proposed by the MaX scientists is complementary to the docking methods used up to now and based on in-silico research of the inhibitor. Biological systems are naturally composed of fragments such as amino-acids in proteins or nitrogenous bases in DNA.

With this approach, it is possible to evaluate whether the amino acid-based fragmentation is consistent with the electronic structure resulting from the QM computation. This is an important indicator for the end-user, as it enables to evaluate the quality of the information associated with a given fragment. Then, QM observables on the system’s fragments can be obtained, which are based on a population analysis of electronic density of the system, projected on the amino-acid.

A novelty that this approach enables is the possibility of quantifying the strength of the chemical interaction between the different fragments. It is possible to select a target region and identify which fragments of the systems interact with this region by sharing electrons with it.

“We can reconstruct the fragmentation of the system in such a way as to focus on an active site in a specific portion of the protein”, says Luigi Genovese from CEA (Commissariat à l’énergie atomique et aux énergies alternatives) who is heading Max’s efforts on this topic. “We think this modelling approach could inform efforts in protein design by granting access to variables otherwise impervious to observation.”

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus, named Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019. The illness caused by this virus has been named coronavirus disease 2019 (COVID-19).
Various EU projects are using supercomputing applications to tackle and support the global challenge of containing the pandemic (c)CDC on Unsplash

Improving drug design and biosensors

The project E-CAM supports HPC simulations in industry and academia through software development, training and discussion in simulation and modeling. Project members are currently following two approaches to add to the research on the corona virus.

Firstly, the SARS-CoV-2 virus that causes COVID-19 uses a main protease to be functional. One of the drug targets currently under investigation is an inhibitor for this protease. While efforts on simulations of binding stability and dynamics are being conducted, not much is known of the dynamical transitions of the binding-unbinding reaction. Yet, this knowledge is crucial for improved drug design. E-CAM aims to shed light on these transitions, using a software package developed by project teams at the University of Amsterdam and the Ecole Normale Superieure in Lyon.

Secondly, E-CAM contributes to the development of the software required to design a protein-based sensor for the quick detection of COVID-19. The sensor, developed at the partner University College Dublin with the initial purpose to target influenza, is now being adapted to SARS-CoV-2. This adaptation needs DNA sequences as an input for suitable antibody-epitope pairs. High-performance computing is required to identify these DNA sequences to design and simulate the sensors prior to their expression in cell lines, purification and validation.

Studying COVID-19 infections on the cell level

The project PerMedCoE aims to optimise codes for cell-level simulations in high-performance computing, and to bridge the gap between organ and molecular simulations. The project started in October 2020.

“Multiscale modelling frameworks prove useful in integrating mechanisms that have very different time and space scales, as in the study of viral infection, human host cell demise and immune cells response. Our goal is to provide such a multiscale modelling framework that includes infection mechanisms, virus propagation and detailed signalling pathways,” says Alfonso Valencia, PerMedCoE project coordinator at the Barcelona Supercomputing Center.

The project team has developed a use case that focusses on studying COVID-19 infections using single-cell data. The work was presented to the research community at a specialized virtual conference in November, the Disease Map Community Meeting. “This use case is a priority in the first months of the project”, says Valencia.

On the technical level, disease maps networks will be converted to models of COVID-19 and human cells from the lung epithelium and the immune system. Then, the team will use omics data to personalise models of different patients’ groups, differentiated for example by age or gender. These data-tailored models will then be incorporated into a COVID-focussed version of the open source cell-level simulator PhysiCell.

Supporting policy makers and governments

The HiDALGO project focusses on modelling and simulating the complex processes which arise in connection with major global challenges. The researchers have developed the Flu and Coronavirus Simulator (FACS) with the objective to support decision makers to provide an appropriate response to the current pandemic situation taking into account health and care capabilities.

FACS is guided by the outcomes of SEIR (Susceptible-Exposed-Infectious-Recovered) models operating at national level. It uses geospatial data sources from Openstreet Map to approximate the viral spread in crowded places, while trading the potential routes to reach them.

In this way, the simulator can model the COVID-19 spread at local level to provide estimations of infections and hospital arrivals, given a range of public health interventions, going from no interventions to lockdowns. Public authorities can use the results of the simulations to identify peaks of contagion, set appropriate measures to reduce spread and provide necessary means to hospitals to prevent collapses. “FACS has enabled us to forecast the spread of COVID-19 in regions such as the London Borough of Brent. These forecasts have helped local National Health Service Trusts to more effectively plan out health and care services in response to the pandemic.” says Derek Groen from the HiDALGO project partner Brunel University London.

Scientists from the HiDALGO project use simulations to predict the spread of the Corona virus in certain areas of London. (c)HiDALGO
Scientists from the HiDALGO project use simulations to predict the spread of the Corona virus in certain areas of London. (c)HiDALGO

EXCELLERAT is a project that is usually focussing on supercomputing applications in the area of engineering. Nevertheless, a group of researchers from EXCELLERAT’s consortium partner SSC-Services GmbH, an IT service provider in Böblingen, Germany and the High-Performance Computing Center Stuttgart (HLRS) are also providing measures to contain the pandemic by supporting the German Federal Institute for Population Research (Bundesinstitut für Bevölkerungsforschung, BiB).

The scientists have developed an intelligent data transfer platform, which enables the BiB to upload data, perform computing-heavy simulations on the HLRS’ supercomputer Hawk, and download the results. The platform supports the work of BiB researchers in predicting the demand for intensive care units during the COVID-19 pandemic. “Nowadays, organisations face various issues while dealing with HPC calculations, HPC in general or even the access to HPC resources,” said Janik Schüssler, project manager at SSC Services. “In many cases, calculations are too complex and users do not have the required know-how with HPC technologies. This is the challenge that we have taken on. The BiB’s researchers had to access HLRS’s Hawk in a very complex way. With the help of our new platform, they can easily access Hawk from anywhere and run their simulations remotely.”

“This platform is part of EXCELLERAT’s overall strategy and tools development, which not only addresses the simulation part of engineering workflows, but provides users the necessary means to optimise their work”, said Bastian Koller, Project Coordinator of EXCELLERAT and HLRS’s Managing Director. “Extending the applicability of this platform to further use cases outside of the engineering domain is a huge benefit and increases the impact of the work performed in EXCELLERAT.”

VECMA and CompBioMed join forces to predict the impact of COVID

19. November 2020

Scientists from VECMA and CompBioMed analyzed a model that predicts the spread of the corona virus by undertaking an extensive parametric sensitivity analysis and uncertainty quantification of the publicly available code.

>> Read More

ETP4HPC handbook 2020 released

6. November 2020

The 2020 edition of the ETP4HPC Handbook of HPC projects is available. It offers a comprehensive overview over the European HPC landscape that currently consists of around 50 active projects and initiatives. Amongst these are the 14 Centres of Excellence and FocusCoE, that are also represented in this edition of the handbook.

>> Read here

HPC Centres of Excellence @ Supercomputing '20

4. November 2020

Due to restrictions caused by the global COVID-19 pandemic, the SC20 conference – the world’s leading HPC event – will take place online this year from November 9-19. 

Find below the CoE’s contributions to the 2020 edition of the Supercomputing Conference.

Success Story CompBioMed: Drug Discovery with Janssen Pharmaceutica NV

CoE involved:

CompBioMed is a user-driven Centre of Excellence in Computational Biomedicine. They have users within academia, industry and clinical environments and are working to train more people in the use of their products and methods.

Organisations & Codes Involved:

SURFsara, the domain expert, is the National Supercomputing and e-Science Support Center in the Netherlands. SURFsara provides expertise and services in the areas of High Performance Computing, e-Science & Cloud Services, Data Services, Network support, and Visualisation.

Janssen Pharmaceutica NV, the industrial partner, is an affiliate of the Pharmaceutical branch of the US Johnson & Johnson company. Janssen’s interests are in developing and using advanced molecular simulation methods to optimize lead compounds in discovery programs, predicting the activity of compounds with specific targets.


Janssen’s primary challenge is in developing and using advanced molecular simulation methods to optimize lead compounds in discovery programs. Such methods, if proven robust and accurate could have a profound impact on the way drug discovery is performed. They would permit reliable computational triaging of very close analogue molecules greatly improving efficiency. Also, this would lead to high-confidence design of synthetically more challenging molecules leading to better drugs in new chemical space.


Through the CompBioMed HPC allocations service JAN obtained an allocation of 1.8M core/hours on Cartesius (SURFsara). JAN evaluated the use of non-commercial software for predicting the free energy of a series of compounds and built a protocol which uses open-source software for MD and free energy perturbation simulations. The main simulation code used was GROMACS (version 2016.1) configured to run on GPU, and the results compared to previous simulations performed with Schrodinger’s FEP+ commercial software. Slurm support for job array and job dependencies, has been used to orchestrate and streamline the workflow and to optimise the MD simulations execution on multiple compute nodes.

Scientific impact of this result:

New computational approaches described above can have a major impact on the drug discovery process. Importantly the work studied here will go to the heart of the design-make-test cycle and contribute higher quality methods for compound prioritization.

This is a fundamental issue of drug discovery, because, whilst compounds with acceptable potency can often be found, and found quickly, they do not always come with other desired properties. Hence, during a typical lead optimization (LO) program the challenge often becomes to maintain potency whilst modifying the chemical structure of the lead molecules to overcome these other issues. In this regard computational tools which can accurately predict binding mode combined with accurate binding affinity prediction will be extremely powerful and reduce the number of ‘backwards’ steps required to subsequently move forwards in an LO program.

The project has introduced Janssen to numerous research groups throughout Europe that are able to assist them in their aims and to combine expertise to produce useful results. The use of HPC is critical for these experiments, without which the compute power does not satisfy the needs of such complex systems.

Benefits for further research:

  • Higher quality methods for compound prioritization.
  • Increased efficiency during lead optimisation program.
  • Reduction in time/cost of synthesising redundant drug molecules
  • Higher probability of determining new active drug