Herasymenko, Oleksandra Silva, Madhushika Abu-Saleh, Abd Al-Aziz A. Ahmad, Ayaz Alvarado-Huayhuaz, Jesus Arce, Oscar E. A. Armstrong, Roly J. Arrowsmith, Cheryl Bachta, Kelly E. Beck, Hartmut Berta, Denes Bieniek, Mateusz K. Blay, Vincent Bolotokova, Albina Bourne, Philip E. Breznik, Marko Brown, Peter J. Campbell, Aaron D. G. Carosati, Emanuele Chau, Irene Cole, Daniel J. Cree, Ben Dehaen, Wim Denzinger, Katrin dos Santos Machado, Karina Dunn, Ian Durai, Prasannavenkatesh Edfeldt, Kristina Edwards, Aled Fayne, Darren Felfoldi, Daniel Friston, Kallie Ghiabi, Pegah Gibson, Elisa Geunther, Judith Gunnarsson, Anders Hillisch, Alexander Houston, Douglas R. Jensen, Jan Halborg Harding, Rachel J. Harris, Kate S. Hoffer, Laurent Hogner, Anders Horton, Joshua T. Houliston, Scott Hultquist, Judd F. Hutchinson, Ashley Irwin, John J. Juki, Marko Kandwal, Shubhangi Andrea, Karlova Katis, Vittorio L. Kich, Ryan P. Kireev, Dmitri Koes, David Inniss, Nicole L. Lessel, Uta Liu, Sijie Loppnau, Peter Lu, Wei Martino, Sam Mcgibbon, Miles Meiler, Jens Mettu, Akhila Money-Kyrle, Sam Moretti, Rocco Moroz, Yurii S. Muvva, Charuvaka Newman, Joseph A. Obendorf, Leon Paige, Brooks Pandit, Amit Park, Keunwan Perveen, Sumera Pirie, Rachael Poda, Gennady Protopopov, Mykola Putter, Vera Ricci, Federico Roper, Natalie J. Rosta, Edina Rzhetskaya, Margarita Sabnis, Yogesh Satchell, Karla J. F. Schmitt Kremer, Frederico Scott, Thomas Seitova, Almagul Steinmann, Casper Talagayev, Valerij Tarkhanova, Olga O. Tatum, Natalie J. Treleaven, Dakota Velasque Werhli, Adriano Walters, W. Patrick Wang, Xiaowen Wells, Jude Wells, Geoffrey Westermaier, Yvonne Wolber, Gerhard Wortmann, Lars Zhang, Jixian Zhao, Zheng Zheng, Shuangjia Schapira, Matthieu 2025-07-18T07:00:26Z 2025-07-18T07:00:26Z 2025-07-18 2025-07 1549-9596 https://pubs.kist.re.kr/handle/201004/152790 A critical assessment of computational hit-finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised of computational chemists and data scientists used protein structure and data from fragment-screening paired with advanced computational and machine learning methods to each predict up to 100 inhibitory ligands. Across all teams, 1957 compounds were predicted and were subsequently procured from commercial catalogs for biophysical assays. Of these compounds, 0.7% were confirmed to bind to Nsp13 in a surface plasmon resonance assay. The six best-performing computational workflows used fragment growing, active learning, or conventional virtual screening with and without complementary deep-learning scoring functions. Follow-up functional assays resulted in identification of two compound scaffolds that bound Nsp13 with a K d below 10 mu M and inhibited in vitro helicase activity. Overall, CACHE #2 participants were successful in identifying hit compound scaffolds targeting Nsp13, a central component of the coronavirus replication-transcription complex. Computational design strategies recurrently successful across the first two CACHE challenges include linking or growing docked or crystallized fragments and docking small and diverse libraries to train ultrafast machine-learning models. The CACHE #2 competition reveals how crowd-sourcing ligand prediction efforts using a distinct array of approaches followed with critical biophysical assays can result in novel lead compounds to advance drug discovery efforts. English American Chemical Society CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13 Article 10.1021/acs.jcim.5c00535 1 Journal of Chemical Information and Modeling, v.65, no.13, pp.6884 - 6898 Journal of Chemical Information and Modeling 65 13 6884 6898 N scie scopus 001514111300001 Chemistry, Medicinal Chemistry, Multidisciplinary Computer Science, Information Systems Computer Science, Interdisciplinary Applications Pharmacology & Pharmacy Chemistry Computer Science Article DRUG DISCOVERY