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

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

  • Pharmaceuticals (Basel). 2024 Feb 12;17(2):240. doi: 10.3390/ph17020240.
Karina Eurídice Juárez-Mercado 1,  Milton Abraham Gómez-Hernández 2 3,  Juana Salinas-Trujano 4 5,  Luis Córdova-Bahena 2 6,  Clara Espitia 7,  Sonia Mayra Pérez-Tapia 4 5 8,  José L Medina-Franco 1,  Marco A Velasco-Velázquez 2
Affiliations

Affiliations

  • 1 DIFACQUIM Research Group, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
  • 2 School of Medicine, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
  • 3 Graduate Program in Biomedical Sciences, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
  • 4 Research and Development in Biotherapeutics Unit (UDIBI), National School of Biological Sciences, Instituto Politécnico Nacional, Mexico City 11350, Mexico.
  • 5 National Laboratory for Specialized Services of Investigation, Development and Innovation (I+D+i) for Pharma Chemicals and Biotechnological Products, LANSEIDI-FarBiotech-CONACHyT, Mexico City 11350, Mexico.
  • 6 National Council of Humanities, Science and Technology (CONAHCYT), Mexico City 03940, Mexico.
  • 7 Immunology Department, Institute for Biomedical Research, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
  • 8 Immunology Department, National School of Biological Sciences, Instituto Politécnico Nacional, Mexico City 11350, Mexico.
PMID: 38399455 DOI: 10.3390/ph17020240
Abstract

SARS-CoV-2 Main Protease (Mpro) is an Enzyme that cleaves viral polyproteins translated from the viral genome, which is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development. Herein, we performed a large-scale virtual screening by comparing multiple structural descriptors of reference molecules with reported anti-coronavirus activity against a library with >17 million compounds. Further filtering, performed by applying two machine learning algorithms, identified eighteen computational hits as anti-SARS-CoV-2 compounds with high structural diversity and drug-like properties. The activities of twelve compounds on Mpro's enzymatic activity were evaluated by fluorescence resonance energy transfer (FRET) assays. Compound 13 (ZINC13878776) significantly inhibited SARS-CoV-2 Mpro activity and was employed as a reference for an experimentally hit expansion. The structural analogues 13a (ZINC4248385), 13b (ZNC13523222), and 13c (ZINC4248365) were tested as Mpro inhibitors, reducing the enzymatic activity of recombinant Mpro with potency as follows: 13c > 13 > 13b > 13a. Then, their anti-SARS-CoV-2 activities were evaluated in plaque reduction assays using Vero CCL81 cells. Subtoxic concentrations of compounds 13a, 13c, and 13b displayed in vitro Antiviral activity with IC50 in the mid micromolar range. Compounds 13a-c could become lead compounds for the development of new Mpro inhibitors with improved activity against anti-SARS-CoV-2.

Keywords

3CLpro; COVID-19; Mpro; SARS-CoV-2; chemoinformatics; ligand-based drug discovery; virtual screening.

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