Article
Author: Gentile, Francesco ; Chau, Irene ; Schapira, Matthieu ; Lee, Soowon ; Tarkhanova, Olga ; Tararina, Valentyna V. ; Walters, Patrick ; Tingey, Damon ; Della Corte, Dennis ; Sharma, Purshotam ; Chen, Yu ; Fayne, Darren ; Hoffer, Laurent ; Song, Minghu ; Ban, Fuqiang ; Paige, Brooks ; Talagayev, Valerij ; Herasymenko, Oleksandra ; Kozakov, Dima ; Jensen, Jan Halborg ; Cherkasov, Artem ; Arrowsmith, Cheryl ; Ravichandran, Rahul ; Hutchinson, Ashley ; Moretti, Rocco ; Meiler, Jens ; Koirala, Kushal ; Kotelnikov, Sergei ; Lessel, Uta ; Silva, Madhushika ; Steinmann, Casper ; Park, Keunwan ; Hillisch, Alexander ; Seitova, Almagul ; Stevens, Rick ; Sabnis, Yogesh ; Rosta, Edina ; Abu-Saleh, Abd Al-Aziz A. ; Karlova, Andrea ; Tropsha, Alexander ; Poda, Gennady ; Liu, Sijie ; Moroz, Yurii S. ; Günther, Judith ; Doering, Niklas P. ; Edwards, Aled ; Dehaen, Wim ; Beck, Hartmut ; Wortmann, Lars ; Wolber, Gerhard ; Bishop, Kevin P. ; Muvva, Charuvaka ; Edfeldt, Kristina ; Hogner, Anders ; Bolotokova, Albina ; Oprea, Tudor I. ; Gibson, Elisa ; Trant, John F. ; Gokdemir, Ozan ; Loppnau, Peter ; Breznik, Marko ; Scott, Thomas ; Zheng, Shuangjia ; Harding, Rachel J. ; Rognan, Didier ; Pandit, Amit ; Gunnarsson, Anders ; Ashworth, Alan ; Lee, Juyong ; Fraser, James S. ; Liu, Xuefeng ; Correy, Galen J. ; Kandwal, Shubhangi ; Bohórquez, Hugo J. ; Pütter, Vera ; Treleaven, Dakota ; Protopopov, Mykola V. ; Wells, Jude ; Denzinger, Katrin ; Westermaier, Yvonne ; Irwin, John J. ; Sindt, François ; Ackloo, Suzanne
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.