Discovery of SARS-CoV-2 Nsp14-Methyltransferase (MTase) Inhibitors by Harnessing Scaffold-Centric Exploration of the Ultra Large Chemical Space
Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide The global impact of SARS-CoV-2 underscores the need for antiviral treatments beyond vaccines. This study targets Nsp14-MTase, a viral protein essential for replication. Initial quantitative high-throughput screening (qHTS) of ∼15,000 compounds from the selected NCATS in-house libraries identified 135 active hit molecules, reflecting a hit-rate of 1.04%. To enhance the search for promising antiviral agents, we expanded this screening campaign with two rounds of machine learning (ML)-based virtual screening of ∼130,000 compounds. The first iteration yielded 72 active compounds encompassing 27 chemotypes with an IC 50 ranging from 1.45 μM to 33.27 μM, increasing the hit-rate 28-fold over the initial qHTS screen. Scaffold clustering of those hits revealed 27 chemotypes. The second iteration added 30 more hits (IC 50: 2.18 μM–30.79 μM) across 12 new chemotypes. Initial structure–activity relationship (SAR) exploration around selected chemotypes identified NCGC00606183 (IC 50: 0.41 μM) as the most potent hit. Hit-to-lead optimization using scaffold-centric exploration against the ultra large Enamine REAL Space (∼5.6 billion compounds) in HPC clusters identified 78 analogs, with 56 showing potent biochemical activity (IC 50: 0.12 μM–18.23 μM) and cellular activity (0.27 μM–23.07 μM) in fully infectious SARS-CoV-2 live virus assays.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409785762