Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors

作者：Sourav Pal, Kellie D. Nance, Dirgha Raj Joshi, Stephen C. Kales, Lin Ye, Xin Hu, Khalida Shamim, Alexey Zakharov · 发表于：Journal of Chemical Information and Modeling · 年份：2025 · DOI：10.1021/acs.jcim.4c02126 · 被引用次数：7 · 研究领域：Computational Drug Discovery Methods、SARS-CoV-2 and COVID-19 Research、Machine Learning in Bioinformatics

The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced Paxlovid, an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this study focuses on designing new antivirals against another protease, papain-like protease (PLpro), which is crucial for viral replication and immune suppression. NCATS/NIH performed a high-throughput screen of ∼15,000 molecules from an internal molecular library, identifying initial hits with a 0.5% success rate. To improve the hit rate and identify potent inhibitors, machine learning-based virtual screens were applied to ∼150,000 compounds, yielding 125 top predicted hits. Biochemical evaluation revealed 25 promising compounds, with a 20% hit-rate and IC 50 values from 1.75 μM to <36 μM across 13 chemotypes. Further analog screening of those chemotypes, as part of the structure–activity relationships, led to 20 additional hits. Additionally, the hit-to-lead optimization of chemotype 7 produced 10 more analogs. These PLpro inhibitors provide promising templates for antiviral development against COVID-19.