Identification of SARS-CoV-2 Main Protease Inhibitors Using Chemical Similarity Analysis Combined with Machine...

Area of Science:

• Drug Discovery and Development
• Virology
• Computational Chemistry

Background:

• SARS-CoV-2 Main Protease (Mpro) is essential for viral replication and a key target for antiviral drug development.
• A large library of compounds was screened to identify potential inhibitors of Mpro.

Purpose of the Study:

• To identify novel SARS-CoV-2 Mpro inhibitors through large-scale virtual screening and machine learning.
• To evaluate the in vitro antiviral activity of identified compounds and their analogues.

Main Methods:

• Virtual screening of over 17 million compounds using structural descriptors.
• Machine learning algorithms for hit identification.
• Fluorescence resonance energy transfer (FRET) assays to assess Mpro enzymatic inhibition.
• Plaque reduction assays to evaluate in vitro antiviral activity.

Main Results:

• Eighteen computational hits with drug-like properties were identified.
• Compound 13 demonstrated significant inhibition of SARS-CoV-2 Mpro.
• Analogues 13a, 13b, and 13c also inhibited Mpro activity, with 13c being the most potent.
• Compounds 13a, 13b, and 13c exhibited in vitro antiviral activity against SARS-CoV-2.

Conclusions:

• The identified compounds, particularly 13a-c, show promise as lead compounds for developing new Mpro inhibitors.
• Further optimization of these compounds could lead to effective anti-SARS-CoV-2 therapeutics.