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Prioritisation of Compounds for 3CLpro Inhibitor Development on SARS-CoV-2 Variants.

Marko Jukič1,2, Blaž Škrlj3, Gašper Tomšič4

  • 1Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia.

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PubMed
Summary
This summary is machine-generated.

New severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants threaten vaccine efficacy. Targeting the 3CL protease (3CLpro), which lacks mutations in variants, offers a trans-variant therapeutic strategy.

Keywords:
3C-like protease3CLproCOVID-19MproSARS-CoV-2chemical library designcompound prioritisationhigh-throughputin silico drug designinhibitorsmachine learningvirtual screening

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Area of Science:

  • Virology
  • Drug Discovery
  • Computational Chemistry

Background:

  • COVID-19, caused by SARS-CoV-2, poses a significant global health threat.
  • Emergent SARS-CoV-2 variants (e.g., B.1.1.7, B.1.351, P.1, B.1.617) exhibit mutations that may reduce the effectiveness of current vaccines and therapeutics.
  • The viral 3CL protease (3CLpro) is a crucial enzyme for viral replication and a conserved therapeutic target, as it lacks mutations in key variants.

Purpose of the Study:

  • To identify and prioritize potential 3CLpro inhibitors with trans-variant effectiveness against SARS-CoV-2.
  • To develop a focused library of 3CLpro inhibitors using virtual screening and machine learning.
  • To assess the current landscape of 3CLpro inhibitors and their representation in drug discovery efforts.

Main Methods:

  • High-throughput virtual screening of an
  • In-Stock
  • chemical library using CmDock.
  • Machine learning-based classification and activity regression to enrich prioritized compound libraries.
  • Classification of virtual screening hits based on 208 chemical descriptors to categorize them as 3CLpro inhibitors, viral cysteine protease inhibitors, or other chemical spaces.

Main Results:

  • Prioritized lists of potential 3CLpro inhibitors were generated through integrated virtual screening and machine learning.
  • Analysis revealed that the chemical space of 3CLpro inhibitors is significantly under-represented compared to broader viral cysteine protease inhibitors.
  • The developed methodology for library preparation and compound prioritization is proposed to be superior to using generic commercial libraries.

Conclusions:

  • The 3CLpro remains a promising therapeutic target for developing pan-variant COVID-19 treatments.
  • Integrated computational approaches, including virtual screening and machine learning, are effective for discovering and prioritizing novel drug candidates.
  • Further research and development are needed to address the under-representation of 3CLpro inhibitors in drug discovery pipelines.