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3. A community effort to discover small molecule SARS-CoV-2 inhibitors

Author

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. Kornoushenko, Mikita 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 A. Antonopoulos, Nikolaos E. Epitropakis, Agamemnon K. Krasoulis, Vassilis P. Pitsikalis, Stavros T. 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 III, Babak Falsafi, Bryan Ford, Adolfo García-Sastre, Sang Yup Lee, Nadia Naffakh, Alexandre Varnek, Günter Klambauer, and Thomas M. Hermans

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 a 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 potentially active 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 activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.

Published
2023
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4. Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks

Author

Michael Widrich, Markus Hofmarcher, Andreas Mayr, Johannes Schimunek, Günter Klambauer, Sepp Hochreiter, Elisabeth Rumetshofer, Philipp Renz, Peter Ruch, Andreu Vall, and Philipp Seidl

Subjects
FOS: Computer and information sciences, Virtual screening, Computer Science - Machine Learning, Computer science, business.industry, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Deep learning, Biomolecules (q-bio.BM), Machine Learning (stat.ML), Computational biology, Zinc database, Ligand (biochemistry), medicine.disease_cause, Quantitative Biology - Quantitative Methods, Machine Learning (cs.LG), Quantitative Biology - Biomolecules, Statistics - Machine Learning, FOS: Biological sciences, medicine, Deep neural networks, Artificial intelligence, business, Quantitative Methods (q-bio.QM), Coronavirus
Abstract

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai., Additional results added. Various corrections to formulations and typos

Published
2020

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