Lessons learned from urgent computing in Europe: Tackling the COVID-19 pandemic

Biomolecular Research.

The biological aspects focused on two main areas of research: a structural one concerned with aspects of the spike protein in the outer wall of the virus (ACE2 receptor) and a pharmaceutical one aimed at drug development. Structural modeling gave the best results in the very porous region of glycan shields: Experiments cannot fully identify these regions, making computational analysis crucial. The numerous biostructural applications focus on molecular mechanisms related to biological function, such as recognition, inhibition, binding, or fusion. They aim at characterizing interactions between relevant macromolecules, their dynamics and conformational changes, and their binding pockets.

Docking/Screening.

A subfield related to drug discovery is the (virtual) screening of compounds from existing databases, either to identify new potent compounds associated with a particular disease or to repurpose drugs that may be effective against new targets not previously considered. Researchers in this subfield must take special care, as docking and screening approaches usually yield (many) hits due to the intrinsic stickiness of molecules, even for compounds that later turn out to be irrelevant for medical purposes. Only subsequent experimental (or to a lesser extent independent computational) testing allows an assessment of their eventual relevance. Initially, researchers performed docking runs on the ACE2 spike sites (protein only), but as the structural models developed and improved further they needed to include the shielding glycans (2) surrounding the spike to find realistic drugs for these receptors, and a feedback loop between the structural features of the virus and drug development would increase the number of successful hits. This crucial role of shielding sugars will probably be one of the most relevant transformations in virtual drug screening in the coming years. One of the projects aimed at finding an antiviral therapy uses a protein structure prediction platform based on machine-learning techniques to help design peptides. Our Fast Track Call received 17 proposals in this area, several with overlapping strategies and pools of compounds. As the scientific committee explicitly required experimental validation, the number of projects in the screening category that received computational resources under the Fast Track Call was limited to six.

Many initiatives specifically targeting compound screening have meanwhile come to fruition, such as the JEDI billion molecules against COVID-19 challenge or the #EuVsVirus initiative from the European Commission, in which PRACE partners provided Tier-0 and Tier-1 HPC resources and user support services during a hackathon. The European Union (EU) reported some progress in this direction, as noted by the Exscalate4CoV consortium. Japan installed its most powerful supercomputer, Fukagu, during the pandemic, and the United States also extensively used HPC resources for COVID-19 docking.

Fluid Dynamics.

The initial lines of research neglected the possible areas of organ simulations. However, case studies very quickly showed that the disease affects certain organs in particular, and three projects addressing these aspects received an allocation under the Fast Track Call. Simulations of the heart, lungs, associated organs, and their coupling, are of great importance for safety pharmacology in testing new drugs but also for diagnosis and treatment of the disease, which may involve assisted ventilation. The computer codes used to simulate biofluids, fluidstructure interactions, and their effects on normal organ function were capable of running on supercomputers (i.e., they met scalability requirements). Another category grouped projects that dealt with individual-level studies. These included AI use of data of medical origin and the evolution of viruses by mutation. None of the codes for these computations, or the data the researchers fed them, were suitable for use on HPC.

Transmission.

Several projects studied virus transmission via airborne liquid droplets. This process is particularly important in enclosed spaces, such as offices, public transportation, and airplanes, where the directions and positions of ventilation/air conditioning systems can be adjusted to regulate droplet flow to minimize the effects of virus spread. The engineering community uses computational fluid dynamics approaches for this type of research. The associated codes are very ready for HPC applications in terms of scalability and robustness, and three such projects received resources under the Fast Track Call.

Epidemiology.

Finally, in epidemiology, three projects consider machine-learning techniques to model the spread of infections, while the others rely on agent-based simulations and compartmental models such as SIR (Susceptible, Infectious, or Recovered, first introduced in 1927) or SEIR (which contains a new class, exposed: infected but not infectious) (3). Such models are also useful to facilitate lockdown measures if coupled with population mobility and economics (4), but PRACE did not receive a project proposal that goes in this direction, although this type of project has subsequently been successful in other programs. In this area, most codes were not explicitly HPC-enabled but were still successful in the Fast Track Call. In the context of disease diagnosis and prediction, one project uses machine-learning techniques and computed tomography scans augmented with biomechanical simulations of airflow to train AI models.

Lessons Learned.

We describe the key lessons learned in the following paragraphs, and these pertain to

• Implementing a rigorous scientific peer evaluation process with well-defined and controlled inherent biases that is resistant to issues such as opportunistic science

• Ensuring availability and openness of produced data and related metadata

• Adapting and possibly relaxing technical requirements to enable all scientific fields to benefit from computational resources

• Encouraging cross-disciplinary proposals with global approaches

Rigorous Scientific Evaluation.

To conduct the Fast Track Call, a scientific committee implemented a rigorous selection process for which a high level of reviewer engagement was critical, especially in the special circumstances of a widely locked-down planet. People from all walks of life were PRACEs quick, reliable, critical, and supportive partners. The success rate for the COVID-19 Fast Track was less than 50%, based on scientific and/or technical criteria as well as the potential impact on the pandemic. Given the limited computational resources that were available for the Fast Track Call, the scientific committee decided not to duplicate efforts in any particular direction for thematically overlapping proposals. This strategy favors diversity by exploring a wide range of modalities for crisis management rather than focusing on selected ones. The scientific committee was aware that the drawback of this could be that preference is given to the first proposals received on a given topic, and not necessarily to the most mature ones. Another problem is opportunistic science (7)here in the sense of easy access to massive computational resources. The scientific committee filtered out such proposals and sometimes redirected them to PRACE Project Access Calls. For example, docking without external experimental support and testing was rigorously scrutinized, as were medium- to long-term projects unlikely to provide medically tangible data in the short term.

Efficient Sharing of Data.

Data curation is an essential tool for ensuring global coordination. It is critical for effectively addressing research challenges such as this pandemic. To ensure that the research community can fully and quickly utilize the data generated by the urgent computations, curated repositories and an open-science approach with associated metadata are crucial. Data must be reanalyzed and made available with all metadata. The data should be documented as thoroughly as possible to enable their wide use. The Research Data Alliance has provided specific COVID-19 recommendations and guidelines. ^{We have found that in data-centric approaches such as AI the data themselves present many challenges. The lack of metadata standards, manual processes, delays in collection, or portability hinder their FAIRness (findability, accessibility, interoperability, and reuse of digital assets), while it is also very difficult to obtain reliable data related to personal behavior or physical medical data, especially for scientists who need to calibrate models and quantify the errors to make informed predictions. In the case of the PRACE Fast Track, PRACE encouraged storing data together with Fenix and provided enough storage space for all the applicants. PRACE called for this data to be disclosed in accordance with the FAIR principles, to ensure that, for example, runs are not repeated and, more generally, that taxpayers money is spent as transparently as possible.}

Enabling All Scientific Fields to Benefit.

Is such a call equally beneficial to all areas of science? HPC-ready codes are not available for all fields that are potentially useful in combating the pandemic. Therefore, the scientific committee was eager to support certain proposals, even if they were technically less prepared to run on HPC architectures, as long as they were relevant to advancing the science. These were redirected to national resources. More generally, as computers become faster and more sophisticated, their efficient use is a key concern. To this end, traditional research fields need the support of research software engineers in various areas of scientific computing and visualization. These highly complementary skills should be a constitutive part of any supercomputing infrastructure. Finding a career path for well-trained and versatile software engineers is an urgent need, especially in Europe, as demonstrated by the activities of the High-Level Support Teams in the PRACE resource exploitation process (8).

Encouraging Cross-Disciplinary Proposals with Global Approaches.

Applications to the Fast Track Call predominantly targeted well-identified and mostly single-science areas. Ideally, however, more complex and cross-disciplinary models should address the integrated, highly intertwined, and cascading effects that define such a crisis. For example, support for epidemiological models that incorporate economic concerns would be desirable to evaluate lockdown interventions and achieve a much finer spatial and temporal resolution that limits long-term economic implications. The current emergency movement has highlighted the importance of multilayered, global intervention. Such global approaches should be the goal for future urgent computing implementations. Long-term sustainability of these resources and monitoring of global diseases, natural disasters such as earthquakes, and environmental changes during extreme climate events could be additional areas for further support.