@article{Herasymenko_O-2025_207162,
title = {CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13},
author = {Herasymenko, O. and Silva, M. and Abu-Saleh, A. and Ahmad, A. and Alvarado-Huayhuaz, J. and Arce, O. and Armstrong, R. and Arrowsmith, C. and Bachta, K. and Beck, H. and Berta, D. and Bieniek, M. and Blay, V. and Bolotokova, A. and Bourne, P. and Breznik, M. and Brown, P. and Campbell, A. and Carosati, E. and Chau, I. and Cole, D. and Cree, B. and Dehaen, W. and Denzinger, K. and dos Santos Machado, K. and Dunn, I. and Durai, P. and Edfeldt, K. and Edwards, A. and Fayne, D. and Friston, K. and Ghiabi, P. and Gibson, E. and Guenther, J. and Gunnarsson, A. and Hillisch, A. and Houston, D. and Halborg Jensen, J. and Harding, R. and Harris, K. and Hoffer, L. and Hogner, A. and Horton, J. and Houliston, S. and Hultquist, J. and Hutchinson, A. and Irwin, J. and Jukič, M. and Kandwal, S. and Karlova, A. and Katis, V. and Kich, R. and Kireev, D. and Koes, D. and Inniss, N. and Lessel, U. and Liu, S. and Loppnau, P. and Lu, W. and Martino, S. and McGibbon, M. and Meiler, J. and Mettu, A. and Money-Kyrle, S. and Moretti, R. and Moroz, Y. and Muvva, C. and Newman, J. and Obendorf, L. and Paige, B. and Pandit, A. and Park, K. and Perveen, S. and Pirie, R. and Poda, G. and Protopopov, M. and Pütter, V. and Ricci, F. and Roper, N. and Rosta, E. and Rzhetskaya, M. and Sabnis, Y. and Satchell, K. and Schmitt Kremer, F. and Scott, T. and Seitova, A. and Steinmann, C. and Talagayev, V. and Tarkhanova, O. and Tatum, N. and Treleaven, D. and Velasque Werhli, A. and Walters, W. and Wang, X. and Wells, J. and Wells, G. and Westermaier, Y. and Wolber, G. and Wortmann, L. and Zhang, J. and Zhao, Z. and Zheng, S. and Schapira, M.},
month = {jun},
year = {2025},
abstract = {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 Kd below 10 µM 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 ultra-fast 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.},
journal = {Journal of Chemical Information and Modeling},
publisher = {ACS Publications},
url = {https://doi.org/10.1021/acs.jcim.5c00535},
}
