Herasymenko, Oleksandra Silva, Madhushika Correy, Galen J. Abu-Saleh, Abd Al-Aziz A. Ackloo, Suzanne Arrowsmith, Cheryl Ashworth, Alan Ban, Fuqiang Beck, Hartmut Bishop, Kevin P. Bohorquez, Hugo J. Bolotokova, Albina Breznik, Marko Chau, Irene Chen, Yu Cherkasov, Artem Dehaen, Wim Della Corte, Dennis Denzinger, Katrin Doering, Niklas P. Edfeldt, Kristina Edwards, Aled Fayne, Darren Gentile, Francesco Gibson, Elisa Gokdemir, Ozan Gunnarsson, Anders Gunther, Judith Irwin, John J. Jensen, Jan Halborg Harding, Rachel J. Hillisch, Alexander Hoffer, Laurent Hogner, Anders Hutchinson, Ashley Kandwal, Shubhangi Karlova, Andrea Koirala, Kushal Kotelnikov, Sergei Kozakov, Dima Lee, Juyong Lee, Soowon Lessel, Uta Liu, Sijie Liu, Xuefeng Loppnau, Peter Meiler, Jens Moretti, Rocco Moroz, Yurii S. Muvva, Charuvaka Oprea, Tudor I. Paige, Brooks Pandit, Amit Park, Keunwan Poda, Gennady Protopopov, Mykola V. Putter, Vera Ravichandran, Rahul Rognan, Didier Rosta, Edina Sabnis, Yogesh Scott, Thomas Seitova, Almagul Sharma, Purshotam Sindt, Francois Song, Minghu Steinmann, Casper Stevens, Rick Talagayev, Valerij Tararina, Valentyna V. Tarkhanova, Olga Tingey, Damon Trant, John F. Treleaven, Dakota Tropsha, Alexander Walters, Patrick Wells, Jude Westermaier, Yvonne Wolber, Gerhard Wortmann, Lars Zheng, Shuangjia Fraser, James S. Schapira, Matthieu 2026-02-03T07:30:17Z 2026-02-03T07:30:17Z 2026-02-02 2026-02 1549-9596 https://pubs.kist.re.kr/handle/201004/154138 The third Critical Assessment of Computational Hit-finding Experiments (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. English American Chemical Society CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2 Article 10.1021/acs.jcim.5c02441 1 Journal of Chemical Information and Modeling, v.66, no.3, pp.1566 - 1581 Journal of Chemical Information and Modeling 66 3 1566 1581 Y scie 001667628300001 Chemistry, Medicinal Chemistry, Multidisciplinary Computer Science, Information Systems Computer Science, Interdisciplinary Applications Pharmacology & Pharmacy Chemistry Computer Science Article