Protease Inhibitors

Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Medicinal chemistry (Shariqah (United Arab Emirates)) 37957860

INTRODUCTION: Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the ...

Amides Antiviral Agents Artificial Intelligence COVID-19 Humans Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Peptide Hydrolases Protease Inhibitors Pyrazines RNA-Dependent RNA Polymerase SARS-CoV-2

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Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Journal of chemical information and modeling 38520328

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale b...

Antiviral Agents Coronavirus 3C Proteases COVID-19 COVID-19 Drug Treatment Deep Learning Drug Discovery Humans Molecular Docking Simulation Neural Networks, Computer Protease Inhibitors Protein Binding SARS-CoV-2

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Machine learning-based QSAR and LB-PaCS-MD guided design of SARS-CoV-2 main protease inhibitors.

Bioorganic & medicinal chemistry letters 38925524

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (M). The ligand-bi...

Antiviral Agents Azoles Catalytic Domain Coronavirus 3C Proteases COVID-19 Drug Design Humans Isoindoles Machine Learning Molecular Dynamics Simulation Organoselenium Compounds Protease Inhibitors Quantitative Structure-Activity Relationship SARS-CoV-2

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A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods.

SAR and QSAR in environmental research 39077983

The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In this study, we employed two fingerprint descriptors (ECFP_4 and MACCS) to comprehensively character...

Antiviral Agents Betacoronavirus Coronavirus 3C Proteases COVID-19 Cysteine Endopeptidases Machine Learning Protease Inhibitors Quantitative Structure-Activity Relationship SARS-CoV-2 Support Vector Machine

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Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective.

The journal of physical chemistry. B 39484814

The global prevalence of dengue virus (DENV), a widespread flavivirus, has led to varied epidemiological impacts, economic burdens, and health consequences. The alarming increase in infections is exacerbated by the absence of approved antiviral agent...

Antiviral Agents Dengue Virus Machine Learning Molecular Dynamics Simulation Protease Inhibitors Serine Endopeptidases Thermodynamics Viral Protease Inhibitors Viral Proteases

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Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.

Journal of chemical information and modeling 39818814

The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced , an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this st...

Antiviral Agents Coronavirus 3C Proteases Coronavirus Papain-Like Proteases COVID-19 Drug Treatment Drug Discovery High-Throughput Screening Assays Humans Machine Learning Molecular Docking Simulation Protease Inhibitors SARS-CoV-2 Small Molecule Libraries

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Rational design and synthesis of pyrazole derivatives as potential SARS-CoV-2 M inhibitors: An integrated approach merging combinatorial chemistry, molecular docking, and deep learning.

Bioorganic & medicinal chemistry 39929031

The global impact of SARS-CoV-2 has highlighted the urgent need for novel antiviral therapies. This study integrates combinatorial chemistry, molecular docking, and deep learning to design, evaluate and synthesize new pyrazole derivatives as potentia...

Antiviral Agents Combinatorial Chemistry Techniques Coronavirus 3C Proteases Deep Learning Drug Design Humans Molecular Docking Simulation Protease Inhibitors Pyrazoles SARS-CoV-2 Structure-Activity Relationship

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Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease.

International journal of molecular sciences 40243651

In this study, we utilized machine learning techniques to identify potential inhibitors of the MERS-CoV 3CL protease. Among the models evaluated, the Random Forest (RF) algorithm exhibited the highest predictive performance, achieving an accuracy of ...

Antiviral Agents Catalytic Domain Coronavirus 3C Proteases Drug Discovery Humans Machine Learning Middle East Respiratory Syndrome Coronavirus Molecular Docking Simulation Molecular Dynamics Simulation Protease Inhibitors

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Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.

International journal of molecular sciences 40141322

The classification of Hepatitis C virus (HCV) NS3 inhibitors is essential for identifying potential antiviral agents through computational methods. This study aims to develop an optimized machine learning (ML) model using random forest (RF) and molec...

Antiviral Agents DEAD-box RNA Helicases Drug Discovery Hepacivirus Humans Machine Learning Nucleoside-Triphosphatase Protease Inhibitors Random Forest Serine Endopeptidases Viral Nonstructural Proteins Viral Proteases

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A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.

European journal of medicinal chemistry 40239482

Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituent optimization, a process that is often both time-consuming and expensive. To address this challenge...

Antiviral Agents Coronavirus 3C Proteases COVID-19 COVID-19 Drug Treatment Deep Learning Drug Discovery Humans Molecular Structure Protease Inhibitors SARS-CoV-2 Structure-Activity Relationship

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Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Machine learning-based QSAR and LB-PaCS-MD guided design of SARS-CoV-2 main protease inhibitors.

A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods.

Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective.

Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.

Rational design and synthesis of pyrazole derivatives as potential SARS-CoV-2 M inhibitors: An integrated approach merging combinatorial chemistry, molecular docking, and deep learning.

Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease.

Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.

A deep learning model for structure-based bioactivity optimization and its application in the bioactivity optimization of a SARS-CoV-2 main protease inhibitor.

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