A community effort in SARS-CoV-2 drug discovery

Johannes Schimunek, Philipp Seidl, Katarina Elez, Tim Hempel, Tuan Le, Frank Noé, Simon Olsson, Lluís Raich, Robin Winter, Hatice Gokcan, Filipp Gusev, Evgeny M. Gutkin, Olexandr Isayev, Maria G. Kurnikova, Chamali H. Narangoda, Roman Zubatyuk, Ivan P. Bosko, Konstantin V. Furs, Anna D. Karpenko, Yury V. KornoushenkoMikita Shuldau, Artsemi Yushkevich, Mohammed B. Benabderrahmane, Patrick Bousquet-Melou, Ronan Bureau, Beatrice Charton, Bertrand C. Cirou, Gérard Gil, William J. Allen, Suman Sirimulla, Stanley Watowich, Nick Antonopoulos, Nikolaos Epitropakis, Agamemnon Krasoulis, Vassilis Itsikalis, Stavros Theodorakis, Igor Kozlovskii, Anton Maliutin, Alexander Medvedev, Petr Popov, Mark Zaretckii, Hamid Eghbal-Zadeh, Christina Halmich, Sepp Hochreiter, Andreas Mayr, Peter Ruch, Michael Widrich, Francois Berenger, Ashutosh Kumar, Yoshihiro Yamanishi, Kam Y.J. Zhang, Emmanuel Bengio, Yoshua Bengio, Moksh J. Jain, Maksym Korablyov, Cheng Hao Liu, Gilles Marcou, Enrico Glaab, Kelly Barnsley, Suhasini M. Iyengar, Mary Jo Ondrechen, V. Joachim Haupt, Florian Kaiser, Michael Schroeder, Luisa Pugliese, Simone Albani, Christina Athanasiou, Andrea Beccari, Paolo Carloni, Giulia D'Arrigo, Eleonora Gianquinto, Jonas Goßen, Anton Hanke, Benjamin P. Joseph, Daria B. Kokh, Sandra Kovachka, Candida Manelfi, Goutam Mukherjee, Abraham Muñiz-Chicharro, Francesco Musiani, Ariane Nunes-Alves, Giulia Paiardi, Giulia Rossetti, S. Kashif Sadiq, Francesca Spyrakis, Carmine Talarico, Alexandros Tsengenes, Rebecca C. Wade, Conner Copeland, Jeremiah Gaiser, Daniel R. Olson, Amitava Roy, Vishwesh Venkatraman, Travis J. Wheeler, Haribabu Arthanari, Klara Blaschitz, Marco Cespugli, Vedat Durmaz, Konstantin Fackeldey, Patrick D. Fischer, Christoph Gorgulla, Christian Gruber, Karl Gruber, Michael Hetmann, Jamie E. Kinney, Krishna M. Padmanabha Das, Shreya Pandita, Amit Singh, Georg Steinkellner, Guilhem Tesseyre, Gerhard Wagner, Zi Fu Wang, Ryan J. Yust, Dmitry S. Druzhilovskiy, Dmitry A. Filimonov, Pavel V. Pogodin, Vladimir Poroikov, Anastassia V. Rudik, Leonid A. Stolbov, Alexander V. Veselovsky, Maria De Rosa, Giada De Simone, Maria R. Gulotta, Jessica Lombino, Nedra Mekni, Ugo Perricone, Arturo Casini, Amanda Embree, D. Benjamin Gordon, David Lei, Katelin Pratt, Christopher A. Voigt, Kuang Yu Chen, Yves Jacob, Tim Krischuns, Pierre Lafaye, Agnès Zettor, M. Luis Rodríguez, Kris M. White, Daren Fearon, Frank Von Delft, Martin A. Walsh, Dragos Horvath, Charles L. Brooks, Babak Falsafi, Bryan Ford, Adolfo García-Sastre, Sang Yup Lee, Nadia Naffakh, Alexandre Varnek, Günter Klambauer, Thomas M. Hermans

Research output: Contribution to journalArticlepeer-review

Abstract

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the “Billion molecules against COVID-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

Original languageEnglish
Article numbere202300262
JournalMolecular Informatics
Volume43
Issue number1
DOIs
StatePublished - Jan 2024

Keywords

  • COVID-19
  • SARS-CoV-2
  • drug discovery
  • machine learning

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