Search

Additional file 1: Fig. S1. Fragments used as a basis for the virtual screening. Table S1. 99th percentile of TransFS and SuCOS scores per fragment. Fig. S2. Top scoring compounds by dcTMD. Table S2. Links for accessing the workflows.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available molecules has dramatically increased over the last decade, redefining the problem of compound accessibility as a matter of budget. In this paper we show that if compound prices are the desired proxy for compound availability, then synthetic accessibility scores are not effective strategies for us in selection. Our approach, CopriNet, is a retrosynthesis-free deep learning model trained on 2D graph representations of compounds alongside their prices extracted from the Mcule catalogue. We show that CoPriNet provides price predictions that correlate far better with actual compound prices than any synthetic accessibility score. Moreover, unlike standard retrosynthesis methods, CoPriNet is rapid, with execution times comparable to popular synthetic accessibility metrics, and thus is suitable for high-throughput experiments including virtual screening and de novo compound generation. While the Mcule catalogue is a proprietary dataset, the CoPriNet source code and the model trained on the proprietary data as well as the fraction of the catalogue (100K compound/prices) used as test dataset have been made publicly available at https://github.com/oxpig/CoPriNet.