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

Author

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

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
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., Comment: Additional results added. Various corrections to formulations and typos

Published
2020

2. ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion

Author

Hinterreiter, Andreas, Ruch, Peter, Stitz, Holger, Ennemoser, Martin, Bernard, Jürgen, Strobelt, Hendrik, and Streit, Marc

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data scientists need to assess classifiers' performances, evaluate their learning behavior over time, and compare different models. Typically, this analysis is based on single-number performance measures such as accuracy. A more detailed evaluation of classifiers is possible by inspecting class errors. The confusion matrix is an established way for visualizing these class errors, but it was not designed with temporal or comparative analysis in mind. More generally, established performance analysis systems do not allow a combined temporal and comparative analysis of class-level information. To address this issue, we propose ConfusionFlow, an interactive, comparative visualization tool that combines the benefits of class confusion matrices with the visualization of performance characteristics over time. ConfusionFlow is model-agnostic and can be used to compare performances for different model types, model architectures, and/or training and test datasets. We demonstrate the usefulness of ConfusionFlow in a case study on instance selection strategies in active learning. We further assess the scalability of ConfusionFlow and present a use case in the context of neural network pruning., Comment: Changes compared to previous version: Reintroduced NN pruning use case; restructured Evaluation section; several additional minor revisions. Submitted as Minor Revision to IEEE TVCG on 2020-07-02

Published
2019
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3. The Interactive Minority Game: a Web-based investigation of human market interactions

Author

Laureti, Paolo, Ruch, Peter, Wakeling, Joseph, and Zhang, Yi-Cheng

Subjects
Nonlinear Sciences - Adaptation and Self-Organizing Systems, Condensed Matter - Statistical Mechanics, Computer Science - Human-Computer Interaction, Physics - Physics and Society
Abstract

The unprecedented access offered by the World Wide Web brings with it the potential to gather huge amounts of data on human activities. Here we exploit this by using a toy model of financial markets, the Minority Game (MG), to investigate human speculative trading behaviour and information capacity. Hundreds of individuals have played a total of tens of thousands of game turns against computer-controlled agents in the Web-based_Interactive Minority Game_. The analytical understanding of the MG permits fine-tuning of the market situations encountered, allowing for investigation of human behaviour in a variety of controlled environments. In particular, our results indicate a transition in players' decision-making, as the markets become more difficult, between deductive behaviour making use of short-term trends in the market, and highly repetitive behaviour that ignores entirely the market history, yet outperforms random decision-making. PACS: 02.50.Le; 89.65.Gh; 89.70.+c Keywords: Decision theory and game theory; Economics and financial markets; Information theory, Comment: 11 pages, 1 table, 5 figures. This replacement contains a few small corrections made during publication

Published
2003
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4. The Interactive Minority Game: Instructions for Experts

Author

Ruch, Peter, Wakeling, Joseph, and Zhang, Yi-Cheng

Subjects
Condensed Matter - Disordered Systems and Neural Networks, Quantitative Finance - Trading and Market Microstructure
Abstract

The Interactive Minority Game (IMG) is an online version of the traditional Minority Game in which human players can enter into competition with the traditional computer-controlled agents. Through the rich (and, importantly, analytically understood) behaviour of the MG, we can explore humans' behaviour in different kinds of market--crowded, efficient, critical--with a high degree of control. To make the game easily understandable even to those who are encountering it for the first time, we have presented the game with a rather simplified interface; in this working paper we explain the underlying technical aspects for those who have experience with the traditional MG., Comment: 5 pages, 1 figure

Published
2002

5. A community effort in SARS‐CoV‐2 drug discovery

Author

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

Published
2023
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6. A community effort to discover small molecule SARS-CoV-2 inhibitors

Author

Schimunek, Johannes, primary, Seidl, Philipp, additional, Elez, Katarina, additional, Hempel, Tim, additional, Le, Tuan, additional, Noé, Frank, additional, Olsson, Simon, additional, Raich, Lluís, additional, Winter, Robin, additional, Gokcan, Hatice, additional, Gusev, Filipp, additional, Gutkin, Evgeny M., additional, Isayev, Olexandr, additional, Kurnikova, Maria G., additional, Narangoda, Chamali H., additional, Zubatyuk, Roman, additional, Bosko, Ivan P., additional, Furs, Konstantin V., additional, Karpenko, Anna D., additional, Kornoushenko, Yury V., additional, Shuldau, Mikita, additional, Yushkevich, Artsemi, additional, Benabderrahmane, Mohammed B., additional, Bousquet-Melou, Patrick, additional, Bureau, Ronan, additional, Charton, Beatrice, additional, Cirou, Bertrand C., additional, Gil, Gérard, additional, Allen, William J., additional, Sirimulla, Suman, additional, Watowich, Stanley, additional, Antonopoulos, Nick A., additional, Epitropakis, Nikolaos E., additional, Krasoulis, Agamemnon K., additional, Pitsikalis, Vassilis P., additional, Theodorakis, Stavros T., additional, Kozlovskii, Igor, additional, Maliutin, Anton, additional, Medvedev, Alexander, additional, Popov, Petr, additional, Zaretckii, Mark, additional, Eghbal-zadeh, Hamid, additional, Halmich, Christina, additional, Hochreiter, Sepp, additional, Mayr, Andreas, additional, Ruch, Peter, additional, Widrich, Michael, additional, Berenger, Francois, additional, Kumar, Ashutosh, additional, Yamanishi, Yoshihiro, additional, Zhang, Kam Y.J., additional, Bengio, Emmanuel, additional, Bengio, Yoshua, additional, Jain, Moksh J., additional, Korablyov, Maksym, additional, Liu, Cheng-Hao, additional, Marcou, Gilles, additional, Glaab, Enrico, additional, Barnsley, Kelly, additional, Iyengar, Suhasini M., additional, Ondrechen, Mary Jo, additional, Haupt, V. Joachim, additional, Kaiser, Florian, additional, Schroeder, Michael, additional, Pugliese, Luisa, additional, Albani, Simone, additional, Athanasiou, Christina, additional, Beccari, Andrea, additional, Carloni, Paolo, additional, D'Arrigo, Giulia, additional, Gianquinto, Eleonora, additional, Goßen, Jonas, additional, Hanke, Anton, additional, Joseph, Benjamin P., additional, Kokh, Daria B., additional, Kovachka, Sandra, additional, Manelfi, Candida, additional, Mukherjee, Goutam, additional, Muñiz-Chicharro, Abraham, additional, Musiani, Francesco, additional, Nunes-Alves, Ariane, additional, Paiardi, Giulia, additional, Rossetti, Giulia, additional, Sadiq, S. Kashif, additional, Spyrakis, Francesca, additional, Talarico, Carmine, additional, Tsengenes, Alexandros, additional, Wade, Rebecca C., additional, Copeland, Conner, additional, Gaiser, Jeremiah, additional, Olson, Daniel R., additional, Roy, Amitava, additional, Venkatraman, Vishwesh, additional, Wheeler, Travis J., additional, Arthanari, Haribabu, additional, Blaschitz, Klara, additional, Cespugli, Marco, additional, Durmaz, Vedat, additional, Fackeldey, Konstantin, additional, Fischer, Patrick D., additional, Gorgulla, Christoph, additional, Gruber, Christian, additional, Gruber, Karl, additional, Hetmann, Michael, additional, Kinney, Jamie E., additional, Padmanabha Das, Krishna M., additional, Pandita, Shreya, additional, Singh, Amit, additional, Steinkellner, Georg, additional, Tesseyre, Guilhem, additional, Wagner, Gerhard, additional, Wang, Zi-Fu, additional, Yust, Ryan J., additional, Druzhilovskiy, Dmitry S., additional, Filimonov, Dmitry A., additional, Pogodin, Pavel V., additional, Poroikov, Vladimir, additional, Rudik, Anastassia V., additional, Stolbov, Leonid A., additional, Veselovsky, Alexander V., additional, De Rosa, Maria, additional, De Simone, Giada, additional, Gulotta, Maria R., additional, Lombino, Jessica, additional, Mekni, Nedra, additional, Perricone, Ugo, additional, Casini, Arturo, additional, Embree, Amanda, additional, Gordon, D. Benjamin, additional, Lei, David, additional, Pratt, Katelin, additional, Voigt, Christopher A., additional, Chen, Kuang-Yu, additional, Jacob, Yves, additional, Krischuns, Tim, additional, Lafaye, Pierre, additional, Zettor, Agnès, additional, Rodríguez, M. Luis, additional, White, Kris M., additional, Fearon, Daren, additional, Von Delft, Frank, additional, Walsh, Martin A., additional, Horvath, Dragos, additional, Brooks III, Charles L., additional, Falsafi, Babak, additional, Ford, Bryan, additional, García-Sastre, Adolfo, additional, Lee, Sang Yup, additional, Naffakh, Nadia, additional, Varnek, Alexandre, additional, Klambauer, Günter, additional, and Hermans, Thomas M., additional

Published
2023
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7. ConfusionFlow: A Model-Agnostic Visualization for Temporal Analysis of Classifier Confusion

Author

Hinterreiter, Andreas; https://orcid.org/0000-0003-4101-5180, Ruch, Peter, Stitz, Holger; https://orcid.org/0000-0002-4742-2636, Ennemoser, Martin, Bernard, Jürgen; https://orcid.org/0000-0001-8741-9709, Strobelt, Hendrik; https://orcid.org/0000-0002-8995-1683, Streit, Marc; https://orcid.org/0000-0001-9186-2092, Hinterreiter, Andreas; https://orcid.org/0000-0003-4101-5180, Ruch, Peter, Stitz, Holger; https://orcid.org/0000-0002-4742-2636, Ennemoser, Martin, Bernard, Jürgen; https://orcid.org/0000-0001-8741-9709, Strobelt, Hendrik; https://orcid.org/0000-0002-8995-1683, and Streit, Marc; https://orcid.org/0000-0001-9186-2092

Abstract

Classifiers are among the most widely used supervised machine learning algorithms. Many classification models exist, and choosing the right one for a given task is difficult. During model selection and debugging, data scientists need to assess classifiers' performances, evaluate their learning behavior over time, and compare different models. Typically, this analysis is based on single-number performance measures such as accuracy. A more detailed evaluation of classifiers is possible by inspecting class errors. The confusion matrix is an established way for visualizing these class errors, but it was not designed with temporal or comparative analysis in mind. More generally, established performance analysis systems do not allow a combined temporal and comparative analysis of class-level information. To address this issue, we propose ConfusionFlow, an interactive, comparative visualization tool that combines the benefits of class confusion matrices with the visualization of performance characteristics over time. ConfusionFlow is model-agnostic and can be used to compare performances for different model types, model architectures, and/or training and test datasets. We demonstrate the usefulness of ConfusionFlow in a case study on instance selection strategies in active learning. We further assess the scalability of ConfusionFlow and present a use case in the context of neural network pruning.

Published
2022

9. Automated Model Building for Drug Target Prediction

Author

Ruch, Peter

Subjects
machine learning, deep learning, drug target prediction, neural networks
Abstract

In den letzen Jahren wurden Deep Learning Methoden erfolgreich zur Vorhersage von möglichen Zielen für pharmazeutische Wirkstoe eingesetzt, und es wurde gezeigt dass neuronale Netze zu den besten Methoden für dieses Problem gehören [45]. Es ist bekannt dass Deep Learning Methoden sehr gut mit der Größe des Datensatzes skalieren, wodurch sich die Frage stellt ob mit zusätzlichen Daten die Resultate weiter verbessert werden können. Viele öentlich verfügbare chemische Datenbanken ermöglichen es neue Datensätze für dieses Anwendung zu erstellen wobei dieser Prozess in der Praxis aber sehr arbeits- und zeitintensiv ist. Es gibt zur Zeit kein Softwaresystem, welches diese Aufgabe vereinfachen würde, und das Durchführen neuer Experimente erleichtern würde. Diese Arbeit präsentiert eine Plattform zur automatisierten Erstellung von Modellen für die Vorhersage möglicher Ziele für pharmazeutische Wirkstoe. Die Plattform kann zur Generierung neuer Datensätze für dieses Problem verwendet werden und es ist nun möglich mit unterschiedlichen Protokollen zur Datensetgenerierung zu experimentieren, Daten von verschiedenen Datenbanken zu kombinieren und rasch eine große Anzahl chemischer Deskriptoren zu berechnen. In der Vergangenheit waren derartige Experimente aufgrund des großen Arbeits- und Zeitaufwandes nicht praktikabel. Mithilfe der Plattform dauert die Erstellung von neuen Datensatzes nun nur noch wenige Tage anstatt Wochen. Die resultierenden Datensätze können dann verwendet werden um neue Modelle für die Vorhersage von möglichen Zielen für pharmazeutische Wirkstoe zu erstellen. Als Demonstration wird die Plattform verwendet um für die letzen fünf Versionen (21-25) der ChEMBL Datenbank [20][23] je einen neuen Datensatz zu erstellen. Die Datensätze werden auf Basis ihrer Zusammensetzung anhand von chemischen Verbindungen und experimenteller Messungen analysiert und zum Trainieren von mehreren multitask Neuronalen Netzen verwendet, um den Einuss der Struktur des Datensatzes auf die Performanz der Modelle zu untersuchen. Die Experimente zeigen, dass die Modelle, welche auf die fünf Datensätze trainiert wurden, ähnliche Resultate liefern wie eine vorherige Studie von Mayr et al. [45]. In the last years deep neural networks have been successfully applied to drug target prediction tasks [44] and have been shown to rank among the best methods for this problem [45]. As deep learning methods are known to scale well to large datasets, this begs the question whether results can be further improved using additional data. The many publicly available chemical databases provide a great opportunity to compile new datasets for drug target prediction tasks, but in practice extracting such a dataset is labour and time intensive. There currently exists no framework that could be used to facilitate this task, and enable experimentation using new datasets. This work presents a platform for automated model building for drug target prediction. The platform can be used to automatically extract new datasets for structure activity prediction tasks and allows to freely experiment using dierent protocols for extracting a dataset, combine data from dierent databases, and quickly compute a wide range of chemical descriptors. In the past due to the large amount of work and time involved in compiling of a dataset these experiments were infeasible. Compiling a new dataset using the platform now only requires a few days instead of weeks. The resulting datasets can then be used to build new models for drug target prediction tasks. To demonstrate the platform it is used to extract a dataset from each of the last ve releases (21-25) of the ChEMBL [20][23] database. The thesis explores whether fully automatically extracted datasets can be used for building drug target prediction models straight away or whether they require additional post-processing. The resulting datasets are analysed in terms of their assay and compound composition and used to train several multi-task neural networks in order to evaluate how dataset composition aects performance of the models. The experiments show that models trained on each of the ve extracted datasets achieve similar performance to previous results by Mayr et al. [45]. submitted by Peter Karl Ruch, BSc. Universität Linz, Masterarbeit, 2020 (VLID)4691176

Published
2020

13. A community effort in SARS‐CoV‐2 drug discovery

Author

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

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.

Published
2024
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