(1301-B) Harnessing the power of qHTS and Machine Learning for the discovery SARS-CoV-2 Papain-Like protease inhibitors
Monday, February 5, 2024
2:00 PM – 3:00 PM EST
Location: Exhibit Halls AB
Abstract: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has highlighted the urgent need for effective and safe antiviral therapeutics. One target for this virus, the papain-like protease (PLpro) plays a vital role in viral protein processing, viral replication, and host immune evasion. While an attractive target for drug development, safe and efficacious PLpro inhibitors have proven elusive. In this study, we employed a high-throughput screening (HTS) approach to identify potential PLpro inhibitors from diverse chemical libraries.
First, a miniaturized biochemical HTS assay was designed and optimized for the rapid evaluation of compound libraries. A total of 91,000 compounds were screened, and 257 hit compounds were identified. Next, we developed a suite of follow-up assays to evaluate these hit compounds and validate them as effective PLpro inhibitors. These results were used to develop a machine-learning algorithm for the identification of chemically and spatially similar molecules within internal and external compound libraries. Lastly, these hit compounds were assessed for antiviral effect in a SARS-CoV-2 live-virus assay.
In conclusion, our high-throughput screening approach has identified a novel set of small-molecule inhibitors targeting SARS-CoV-2 PLpro. These inhibitors are promising as potential antiviral therapeutics for the treatment of COVID-19, and further optimization of these compounds is underway. This study underscores the significance of HTS in rapidly identifying lead compounds with the potential to combat emerging viral threats