CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2

The third <i>Critical Assessment of Computational Hit-finding Experiments</i> (CACHE) challenged computational teams to identify chemically novel ligands targeting the macrodomain 1 of SARS-CoV-2 Nsp3, a promising coronavirus drug target. Twenty-three groups deployed diverse design strategies to collectively select 1739 ligand candidates. While over 85% of the designed molecules were chemically novel, the best experimentally confirmed hits were structurally similar to previously published compounds. Confirming a trend observed in CACHE #1 and #2, two of the best-performing workflows used compounds selected by physics-based computational screening methods to train machine learning models able to rapidly screen large chemical libraries, while four others used exclusively physics-based approaches. Three pharmacophore searches and one fragment growing strategy were also part of the seven winning workflows. While active molecules discovered by CACHE #3 participants largely mimicked the adenine ring of the endogenous substrate, ADP-ribose, preserving the canonical chemotype commonly observed in previously reported Nsp3-Mac1 ligands, they still provide novel structure-activity relationship insights that may inform the development of future antivirals. Collectively, these results show that multiple molecular design strategies can efficiently converge on similar potent molecules.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-01-21
DOI
https://doi.org/10.1021/acs.jcim.5c02441
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
45.17
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article

CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2

Wim Dehaen, Mykola Protopopov, Artem Cherkasov, P. Loppnau et al.
1 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
45.17
article

CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2

Wim Dehaen, Mykola Protopopov, Artem Cherkasov, P. Loppnau, A.M. Edwards, Ozan Gökdemir, Junsu Ko, Yvonne Westermaier, Soowon Lee, Jude Wells, Yogesh Sabnis, Abd Al‐Aziz A. Abu‐Saleh, Dennis Della Corte, Purshotam Sharma, G.J. Correy, Kevin P. Bishop, Jan H. Jensen, John J. Irwin, Olga O. Tarkhanova, Tudor Ionel Oprea, Elissa A. Fink, Darren Fayne, Rocco Moretti, Shuangjia Zheng, Matthieu Schapira, Gerhard Wolber, Edina Rosta, Minghu Song, Patrick Walters, Shubhangi Kandwal, Charuvaka Muvva, Amit Kant Pandit, Alexander Hillisch, Valentyna Tararina, Prof. Dr. Didier Rognan, T. W. Scott, Anders Gunnarsson, Gennady Poda, Oleksandra Herasymenko, Alma Seitova, Niklas Piet Doering, Andrea Karlova, Dakota Treleaven, François Sindt, Rachel Jane Harding, Irene Chau, Alan Ashworth, Hugo Bohorquez, Sijie Liu, Laurent Hoffer, Xuefeng Liu, KUSHAL KOIRALA, Keunwan Park, John F. Trant, Marko Breznik, Hartmut Beck, Anders Hogner, Brooks Paige, Cheryl Arrowsmith, Lars Wortmann, Casper Steinmann, Rahul Ravichandran, Alexander Tropsha, James S. Fraser, Ashley Hutchinson, Valerij Talagayev, Dima Kozakov, Francesco Gentile, Yu Chen, Jens Meiler, Vera Pütter, Damon Tingey, Fuqiang Ban, Uta Lessel, Rick Stevens
article en
1 citations

Abstract

The third <i>Critical Assessment of Computational Hit-finding Experiments</i> (CACHE) challenged computational teams to identify chemically novel ligands targeting the macrodomain 1 of SARS-CoV-2 Nsp3, a promising coronavirus drug target. Twenty-three groups deployed diverse design strategies to collectively select 1739 ligand candidates. While over 85% of the designed molecules were chemically novel, the best experimentally confirmed hits were structurally similar to previously published compounds. Confirming a trend observed in CACHE #1 and #2, two of the best-performing workflows used compounds selected by physics-based computational screening methods to train machine learning models able to rapidly screen large chemical libraries, while four others used exclusively physics-based approaches. Three pharmacophore searches and one fragment growing strategy were also part of the seven winning workflows. While active molecules discovered by CACHE #3 participants largely mimicked the adenine ring of the endogenous substrate, ADP-ribose, preserving the canonical chemotype commonly observed in previously reported Nsp3-Mac1 ligands, they still provide novel structure-activity relationship insights that may inform the development of future antivirals. Collectively, these results show that multiple molecular design strategies can efficiently converge on similar potent molecules.

Journal of Chemical Information and ModelingVol. 66(3)
Brigham Young University (US), Ontario Institute for Cancer Research (CA), University of North Carolina at Chapel Hill (US), University of Copenhagen (DK), Argonne National Laboratory (US), Medpace (United States) (US), University Health Network (CA), Boehringer Ingelheim (Germany) (DE), Karolinska University Hospital (SE), 3M (United States) (US), Seoul National University (KR), University of British Columbia (CA), University of Ottawa (CA), University of California, San Francisco (US), Shanghai Jiao Tong University (CN), University of Toronto (CA), Taras Shevchenko National University of Kyiv (UA), Vanderbilt University (US), Trinity College (CA), Rohde & Schwarz (Germany) (DE), Trinity College Dublin (IE), University of Management and Technology (US), Narsee Monjee Institute of Management Studies (IN), Princess Margaret Cancer Centre (CA), Hydrologic Research Center (US), Health Net (US), Structural Genomics Consortium (CA), Karolinska Institutet (SE), University of Chicago (US), International Life Sciences Institute (US), Relay Therapeutics (United States) (US), AstraZeneca (Brazil) (BR), Laboratoire d'Innovation Thérapeutique (FR), Boehringer Ingelheim (Austria) (AT), Technology Centre Prague (CZ), Prevention of Organ Failure (CA), National Science Centre (PL), Nuvisan (Germany) (DE), The Prostate Centre (GB), AstraZeneca (Germany) (DE), Institute of Mineralogy (RU), Artificial Intelligence in Medicine (Canada) (CA), Bayer (France) (FR), UCSF Helen Diller Family Comprehensive Cancer Center (US), Dublin City University (IE), U-M Rogel Cancer Center (US), Ottawa Institute of Systems Biology, University College London (GB), Korea Institute of Science and Technology (KR), Stony Brook University (US), Université de Strasbourg (FR), University of Windsor (CA), Freie Universität Berlin (DE), UCB Pharma (Belgium) (BE), Aalborg University (DK), University of Chemistry and Technology, Prague (CZ), Leipzig University (DE), Hexcel (United States) (US)
Openalex Percentile: Top 1%
Computational Drug Discovery Methods
45.17
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