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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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
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Abstract	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.
AbstractList	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 μM 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. 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 μM 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.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 μM 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. 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.
Author	Richard-Bildstein, Sylvia Kimmerlin, Thierry Ritz, Daniel Bourquin, Geoffroy Czodrowski, Paul Mac Sweeney, Aengus Regeon, Sylvain Hazemann, Julien Lange, Roland
Author_xml	– sequence: 1 givenname: Julien orcidid: 0009-0008-8946-2514 surname: Hazemann fullname: Hazemann, Julien organization: Idorsia Pharmaceut Ltd, Drug Discovery Chem, CH-4123 Allschwil, Switzerland – sequence: 2 givenname: Thierry surname: Kimmerlin fullname: Kimmerlin, Thierry email: thierry.kimmerlin@idorsia.com organization: Idorsia Pharmaceut Ltd, Drug Discovery Chem, CH-4123 Allschwil, Switzerland – sequence: 3 givenname: Aengus orcidid: 0000-0001-7250-9541 surname: Mac Sweeney fullname: Mac Sweeney, Aengus organization: Idorsia Pharmaceut Ltd, Drug Discovery Biol, CH-4123 Allschwil, Switzerland – sequence: 4 givenname: Geoffroy surname: Bourquin fullname: Bourquin, Geoffroy organization: Idorsia Pharmaceut Ltd, Drug Discovery Biol, CH-4123 Allschwil, Switzerland – sequence: 5 givenname: Roland surname: Lange fullname: Lange, Roland organization: Idorsia Pharmaceut Ltd, Drug Discovery Biol, CH-4123 Allschwil, Switzerland – sequence: 6 givenname: Daniel surname: Ritz fullname: Ritz, Daniel organization: Idorsia Pharmaceut Ltd, Drug Discovery Biol, CH-4123 Allschwil, Switzerland – sequence: 7 givenname: Sylvia orcidid: 0000-0001-6588-1599 surname: Richard-Bildstein fullname: Richard-Bildstein, Sylvia organization: Idorsia Pharmaceut Ltd, Drug Discovery Chem, CH-4123 Allschwil, Switzerland – sequence: 8 givenname: Sylvain surname: Regeon fullname: Regeon, Sylvain organization: Idorsia Pharmaceut Ltd, Drug Discovery Chem, CH-4123 Allschwil, Switzerland – sequence: 9 givenname: Paul orcidid: 0000-0002-7390-8795 surname: Czodrowski fullname: Czodrowski, Paul email: czodpaul@uni-mainz.de organization: Johannes Gutenberg Univ Mainz, Chem Dept, D-55128 Mainz, Germany
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SubjectTerms	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
Title	Accelerating the Hit-To-Lead Optimization of a SARS-CoV-2 Mpro Inhibitor Series by Combining High-Throughput Medicinal Chemistry and Computational Simulations
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