Identification of acrylamide-based covalent inhibitors of SARS-CoV-2 (SCoV-2) Nsp15 using high-throughput screening and machine learning

<3, zero violations to Lipinski's rules, and no apparent pan-assay interference (PAINs) properties. In addition, based on this data as a training set, we developed an artificial intelligence (AI) model that accelerated the hit to lead process and had a 73% accuracy for predicting new acrylamide-based Nsp15 inhibitors. Collectively, these results demonstrate that acrylamide fragments have great potential for developing Nsp15 inhibitors.