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Exscalate4Cov (E4C)
CountryEuropean Union
Launched1st April 2020[1]
Closed30th September 2021[1]
Funding2 970 875 €[1]
StatusProject Closed
Websitehttps://www.exscalate4cov.eu

Exscalate4Cov was a public-private consortium, supported by the Horizon Europe program from the European Union, which aimed at leveraging High-performance computing computational power to fight the coronavirus pandemic. The core of the project leveraged high-throughput extreme-scale computer-aided drug design software to conduct experiments[2].

The Exsclate4Cov (EXaSCale smArt pLatform Against paThogEns for Corona Virus)[1] has been coordinated by Dompé Farmaceutici and involved 17 participants[1]. It was part of Horizon 2020 SOCIETAL CHALLENGES - Health, demographic change and well-being founding[3] founding.

The project saw the execution of one of the largest large scale Virtual screening experiment[4], and also the project identify a molecule which is potentially effective against SARS-CoV-2[5].

Context

Background

Drug discovery can be a long and costly costly[1]. Pharmaceutical companies own large dataset of Chemical compound, that they test against a Drug target, which is often a Protein receptor. Thus, the process of find new drugs usually involves High-throughput screening. For example Virtual screening can be used to evaluate the interactions between large datasets of small Molecules against a Drug target.

In a Urgent computing scenario, where the time to solution matter, Virtual screening is used to identify hit molecules for latter stages of the Drug discovery pipeline. The Exscalate4Cov project took place after the outbreak of the COVID-19 pandemic, with the aim of leveraging EU Supercomputers to find faster solution to pandemic situation faster[2].

Objectives

The projects objectives were:

  • Identify molecules which targets the coronavirus.
  • Conduct a large scale experiments as an example for future pandemic scenario[2].
  • Develop a computer-aided drug design platform which leverage supercomputer capabilities[6].
  • Fast sharing of data and scientific discoveries with the community[7], to work in a urgent computing scenario.

Previous Projects

The Exscalate4Cov project took place after ANTAREX4ZIKA[8] project. They share objectives, but ANTAREX4ZIKA target the zika virus instead. The experiment concluded at the end of 2018, and saw the execution of a virtual screening campaign on CINECA Marconi machine, with a total of 10 PetaFLOPS[8].

Consortium

Exscalate4Cov Consortium Members
Organization Type Industry Country
Dompé Farmaceutici Private Pharmaceutical industry  Italy
CINECA Public research center Supercomputing  Italy
Politecnico di milano Public university Scientific and technological research, education  Italy
University of Milan Public university Scientific and technological research, education  Italy
Katholieke Universiteit, Leuven Public university Scientific and technological research, education  Belgium
International Institute of Molecular and Cell Biology Public research center Research center  Poland
Elettra Sincrotrone Trieste Research Organisations Research center  Italy
Fraunhofer-Gesellschaft Research Organisations Research center  Germany
Barcelona Supercomputing Center Public research center Supercomputing  Spain
Forschungszentrum Jülich Public research center Supercomputing  Germany
University of Naples Federico II Public university Scientific and technological research, education  Italy
University of Cagliari Public university Scientific and technological research, education  Italy
SIB Swiss Institute of Bioinformatics Public research center Research center   Switzerland
KTH Royal Institute of Technology Public university Scientific and technological research, education  Sweden
Lazzaro Spallanzani National Institute for Infectious Diseases Research Organisations Hospital  Italy
Associtazione Big Data Company Other  Italy
Istituto Nazionale di Fisica Nucleare Public research center Research center  Italy
Chelonia SA Company Other   Switzerland

Pipeline

Here we describe the pipeline developed and used in the project's main virtual screening campaign.

At the application level the inputs are the ligand from the chemical space and the protein target of the virtual screening campaign, in this case the spike protein. After a molecular docking stage, which generates possible ligand conformations, scoring functions for docking are used to evaluate the interaction strength between the ligand's pose and the protein. Thus, to produce a ranking of hit compounds.

At the software level the project uses the EXSCALATE platform[4][8], built mainly upon LiGen(Ligand Generator), which is used to perform molecular docking and scoring simulations.

To hinge the computational power offered by HPC center, the project make use of Message Passing Interface[9] to scale multi-node, and CUDA acceleration to take advantage of GPUs in supercomputers. In particular the CUDA has been developed through different optimizations[10][11][12].

Virtual Screening Campaign

The project's main experiment evaluated the interactions between 12 viral Proteins of SARS-CoV-2 against 70 billion molecules from the EXSCALATE[6]Chemical library from Dompé Farmaceutici. In November 2020 members of the consortium coordinated one of the largest Virtual screening campaign, by leveraging two supercomputers, which combined allowed a computational power of 81 PFLOPS[13]. The two supercomputers are:

  • Marconi100: operated by CINECA, each node consists of 1 IBM POWER9 AC922 CPU(32-cores, 128-threads), 4 NVIDIA V100 GPUs with 16 GB of VRAM. The machine consists of 970 nodes(29.3 PetaFLOPS)[14].
  • HPC5: operated by Eni, each node consists of 1 Intel Xeon Gold 6252 24C CPU(24-cores, 48-threads), 4 NVIDIA V100 GPUs with 16 GB of VRAM. The machine consists of 1820 nodes(51.7 PetaFLOPS)[15].

The large scale campaign used a reservation of 800 Marconi100's nodes and 1500 HP5's nodes, for 60 hours[4]. The average throughput has been of 2400 lig/s on Marconi100 and 2000 lig/s on HPC5[4].

The pre-processing(100 nodes) and post-processing(19 nodes) required 5 days of nodes usage each. The resulting dataset containing 570 millions hit compounds is freely available[4].

Results

Mediate

The project's large scale campaign results are available through the MEDIATE(MolEcular DockIng AT homE) platform[16]. The MEDIATE objective is to collect a chemical library of Sars-COV-2 inhibitors. The MEDIATE portal made available the set of small molecules, which research can use to start de-novo drug design from a reduce set of molecules.

Raloxifene

Raloxifene is a known chemical compound used to treat Osteoporosis. The E4C experiment had identified raloxifene as a possible candidate to treat early-stage COVID-19 patients[5][17]. Actually it is under testing for approval[18].

Public Interest

The experiments, and as well the discovery of raloxifene as possible drug candidate against COVID-19, gained the scientific community interested, as documented in a number of scientific article[4][5][17].

The results of the project also gained national interest in Italy on different newspaper[19][20][21], due to the usage of supercomputers located in Italy during the pandemic. The large scale campaign also gained interests from international journal[22][23].

References

  1. ^ a b c d e "EXaSCale smArt pLatform Against paThogEns for Corona Virus | EXSCALATE4CoV Project | Fact Sheet | H2020". CORDIS | European Commission. doi:10.3030/101003551. Retrieved 2024-07-09.
  2. ^ a b c "Science". www.exscalate4cov.eu. Retrieved 2024-07-09.
  3. ^ "SOCIETAL CHALLENGES - Health, demographic change and well-being | Programme | H2020". CORDIS | European Commission. Retrieved 2024-07-09.
  4. ^ a b c d e f Gadioli, Davide; Vitali, Emanuele; Ficarelli, Federico; Latini, Chiara; Manelfi, Candida; Talarico, Carmine; Silvano, Cristina; Cavazzoni, Carlo; Palermo, Gianluca; Beccari, Andrea Rosario (2023-01-01). "EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2". IEEE Transactions on Emerging Topics in Computing. 11 (1): 170–181. doi:10.1109/TETC.2022.3187134. ISSN 2168-6750.
  5. ^ a b c Iaconis, Daniela; Bordi, Licia; Matusali, Giulia; Talarico, Carmine; Manelfi, Candida; Cesta, Maria Candida; Zippoli, Mara; Caccuri, Francesca; Bugatti, Antonella; Zani, Alberto; Filippini, Federica; Scorzolini, Laura; Gobbi, Marco; Beeg, Marten; Piotti, Arianna (2022-05-25). "Characterization of raloxifene as a potential pharmacological agent against SARS-CoV-2 and its variants". Cell Death & Disease. 13 (5): 1–9. doi:10.1038/s41419-022-04961-z. ISSN 2041-4889. PMC 9130985. PMID 35614039.{{cite journal}}: CS1 maint: PMC format (link)
  6. ^ a b "Exscalate | AI Drug Discovery Platform". exscalate.com. Retrieved 2024-07-09.
  7. ^ "Home". mediate.exscalate4cov.eu. Retrieved 2024-07-09.
  8. ^ a b c "Exscalate Projects". www.exscalate.eu. Retrieved 2024-07-09.
  9. ^ Markidis, Stefano; Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca (2021-11). "Understanding the I/O Impact on the Performance of High-Throughput Molecular Docking". IEEE: 9–14. doi:10.1109/PDSW54622.2021.00007. ISBN 978-1-6654-1837-9. {{cite journal}}: Check date values in: |date= (help); Cite journal requires |journal= (help)
  10. ^ Gadioli, Davide; Palermo, Gianluca; Cherubin, Stefano; Vitali, Emanuele; Agosta, Giovanni; Manelfi, Candida; Beccari, Andrea R.; Cavazzoni, Carlo; Sanna, Nico; Silvano, Cristina (2021-01). "Tunable approximations to control time-to-solution in an HPC molecular docking Mini-App". The Journal of Supercomputing. 77 (1): 841–869. doi:10.1007/s11227-020-03295-x. ISSN 0920-8542. {{cite journal}}: Check date values in: |date= (help)
  11. ^ Vitali, Emanuele; Gadioli, Davide; Palermo, Gianluca; Beccari, Andrea; Cavazzoni, Carlo; Silvano, Cristina (2019-07). "Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes". The Journal of Supercomputing. 75 (7): 3374–3396. doi:10.1007/s11227-019-02875-w. ISSN 0920-8542. {{cite journal}}: Check date values in: |date= (help)
  12. ^ Vitali, Emanuele; Ficarelli, Federico; Bisson, Mauro; Gadioli, Davide; Accordi, Gianmarco; Fatica, Massimiliano; Beccari, Andrea R.; Palermo, Gianluca (2024-04-01). "GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis". Journal of Parallel and Distributed Computing. 186: 104819. doi:10.1016/j.jpdc.2023.104819. ISSN 0743-7315.
  13. ^ "EXSCALATE4COV: 60 ORE DI SUPERCALCOLO CONTRO IL CORONAVIRUS".
  14. ^ "HPC5 - PowerEdge C4140, Xeon Gold 6252 24C 2.1GHz, NVIDIA Tesla V100, Mellanox HDR Infiniband | TOP500". www.top500.org. Retrieved 2024-07-09.
  15. ^ "UG3.2: MARCONI100 UserGuide - SCAI - User Support - CINECA Technical Portal". wiki.u-gov.it. Retrieved 2024-07-09.
  16. ^ "Home". mediate.exscalate4cov.eu. Retrieved 2024-07-09.
  17. ^ a b Allegretti, Marcello; Cesta, Maria Candida; Zippoli, Mara; Beccari, Andrea; Talarico, Carmine; Mantelli, Flavio; Bucci, Enrico M.; Scorzolini, Laura; Nicastri, Emanuele (2022-01). "Repurposing the estrogen receptor modulator raloxifene to treat SARS-CoV-2 infection". Cell Death & Differentiation. 29 (1): 156–166. doi:10.1038/s41418-021-00844-6. ISSN 1476-5403. PMC 8370058. PMID 34404919. {{cite journal}}: Check date values in: |date= (help)CS1 maint: PMC format (link)
  18. ^ Nicastri, Emanuele; Marinangeli, Franco; Pivetta, Emanuele; Torri, Elena; Reggiani, Francesco; Fiorentino, Giuseppe; Scorzolini, Laura; Vettori, Serena; Marsiglia, Carolina; Gavioli, Elizabeth Marie; Beccari, Andrea R.; Terpolilli, Giuseppe; De Pizzol, Maria; Goisis, Giovanni; Mantelli, Flavio (2022-06). "A phase 2 randomized, double-blinded, placebo-controlled, multicenter trial evaluating the efficacy and safety of raloxifene for patients with mild to moderate COVID-19". eClinicalMedicine. 48: 101450. doi:10.1016/j.eclinm.2022.101450. ISSN 2589-5370. PMC 9098200. PMID 35582123. {{cite journal}}: Check date values in: |date= (help)CS1 maint: PMC format (link)
  19. ^ "Exscalate, il super software che scopre le molecole contro il Covid-19". Corriere della Sera (in Italian). 2021-10-08. Retrieved 2024-07-09.
  20. ^ "Covid: Aifa, ok a test su Raloxifene in casi lievi - Altre news - Ansa.it". Agenzia ANSA (in Italian). 2020-10-27. Retrieved 2024-07-09.
  21. ^ "Coronavirus, il supercomputer italiano scopre terapia con 'raloxifene'". la Repubblica (in Italian). 2020-06-19. Retrieved 2024-07-09.
  22. ^ Peckham, Oliver (2020-10-29). "Supercomputer Research Leads to Human Trial of Potential COVID-19 Therapeutic Raloxifene". HPCwire. Retrieved 2024-07-09.
  23. ^ Writer, Aila Slisco (2020-06-18). "Osteoporosis Drug Shows Promise in Fighting Coronavirus". Newsweek. Retrieved 2024-07-09.
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