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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Journal of medicinal chemistry Ročník 68; číslo 8; s. 8269 - 8294
Hlavní autori:	Hazemann, Julien, Kimmerlin, Thierry, Mac Sweeney, Aengus, Bourquin, Geoffroy, Lange, Roland, Ritz, Daniel, Richard-Bildstein, Sylvia, Regeon, Sylvain, Czodrowski, Paul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	WASHINGTON Amer Chemical Soc 24.04.2025
Predmet:	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
On-line prístup:	Zistit podrobnosti o prístupe
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	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.
Bibliografia:	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