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

Publication date: Jan 16, 2025

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 values from 1. 75 μM to

Concepts Keywords
Antiviral Compounds
Fda Cov
Library Global
Promising Highlights
Hit
Hits
Inhibitors
Learning
Limited
Plpro
Protease
Rate
Sars
Treatments
Vaccination

Semantics

Type Source Name
drug DRUGBANK Papain
pathway KEGG Viral replication
drug DRUGBANK Spinosad

Original Article

(Visited 1 times, 1 visits today)