Exploration of structure-activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment linking and active learning
Category
Published on
Type
journal-article
Author
Galen J. Correy and Moira M. Rachman and Takaya Togo and Stefan Gahbauer and Yagmur U. Doruk and Maisie G. V. Stevens and Priyadarshini Jaishankar and Brian Kelley and Brian Goldman and Molly Schmidt and Trevor Kramer and Dmytro S. Radchenko and Yurii S. Moroz and Alan Ashworth and Patrick Riley and Brian K. Shoichet and Adam R. Renslo and W. Patrick Walters and James S. Fraser
Citation
Correy, G. J., Rachman, M. M., Togo, T., Gahbauer, S., Doruk, Y. U., Stevens, M. G. V., Jaishankar, P., Kelley, B., Goldman, B., Schmidt, M., Kramer, T., Radchenko, D. S., Moroz, Y. S., Ashworth, A., Riley, P., Shoichet, B. K., Renslo, A. R., Walters, W. P., & Fraser, J. S. (2025). Exploration of structure-activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment linking and active learning. Science Advances, 11(22). https://doi.org/10.1126/sciadv.ads7187
Abstract
The macrodomain of severe acute respiratory syndrome coronavirus 2 nonstructural protein 3 is required for viral pathogenesis and is an emerging antiviral target. We previously performed an x-ray crystallography–based fragment screen and found submicromolar inhibitors by fragment linking. However, these compounds had poor membrane permeability and liabilities that complicated optimization. Here, we developed a shape-based virtual screening pipeline—FrankenROCS. We screened the Enamine high-throughput collection of 2.1 million compounds, selecting 39 compounds for testing, with the most potent binding with a 130 μM median inhibitory concentration (IC
50
). We then paired FrankenROCS with an active learning algorithm (Thompson sampling) to efficiently search the Enamine REAL database of 22 billion molecules, testing 32 compounds with the most potent binding with a 220 μM IC
50
. Further optimization led to analogs with IC
50
values better than 10 μM. This lead series has improved membrane permeability and is poised for optimization. FrankenROCS is a scalable method for fragment linking to exploit synthesis-on-demand libraries.
