Accelerating the Hit-To-Lead Optimization of a SARS-CoV-2 Mpro Inhibitor Series by Combining High-Throughput Medicinal Chemistry and Computational Simulations

In this study, we performed the hit-to-lead optimization of a SARS-CoV-2 Mpro diazepane hit (identified by computational methods in a previous work) by combining computational simulations with high-throughput medicinal chemistry (HTMC). Leveraging the 3D structural information of Mpro, we refined th...

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Veröffentlicht in:Journal of medicinal chemistry Jg. 68; H. 8; S. 8269 - 8294
Hauptverfasser: Hazemann, Julien, Kimmerlin, Thierry, Mac Sweeney, Aengus, Bourquin, Geoffroy, Lange, Roland, Ritz, Daniel, Richard-Bildstein, Sylvia, Regeon, Sylvain, Czodrowski, Paul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: WASHINGTON Amer Chemical Soc 24.04.2025
Schlagworte:
Antiviral Agents - chemistry
Antiviral Agents - pharmacology
Binding Sites
Chemistry, Medicinal
Chemistry, Pharmaceutical
Coronavirus 3C Proteases - antagonists & inhibitors
Coronavirus 3C Proteases - chemistry
Coronavirus 3C Proteases - metabolism
COVID-19 - virology
COVID-19 Drug Treatment
High-Throughput Screening Assays
Humans
Life Sciences & Biomedicine
Machine Learning
Molecular Docking Simulation
Molecular Dynamics Simulation
Pharmacology & Pharmacy
SARS-CoV-2 - drug effects
SARS-CoV-2 - enzymology
Science & Technology
LIBRARIES
ISSN:0022-2623, 1520-4804, 1520-4804
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Beschreibung
Zusammenfassung:In this study, we performed the hit-to-lead optimization of a SARS-CoV-2 Mpro diazepane hit (identified by computational methods in a previous work) by combining computational simulations with high-throughput medicinal chemistry (HTMC). Leveraging the 3D structural information of Mpro, we refined the original hit by targeting the S1 and S2 binding pockets of the protein. Additionally, we identified a novel exit vector pointing toward the S1 ' pocket, which significantly enhanced the binding affinity. This strategy enabled us to transform, rapidly with a limited number of compounds synthesized, a 14 mu M hit into a potent 16 nM lead compound, for which key pharmacological properties were subsequently evaluated. This result demonstrated that combining computational technologies such as machine learning, molecular docking, and molecular dynamics simulation with HTMC can efficiently accelerate hit identification and subsequent lead generation.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0022-2623
1520-4804
1520-4804
DOI:10.1021/acs.jmedchem.4c02941