Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies

Martínez‐Archundia, Marlet; Colín-Astudillo, Brenda; Moreno-Vargas, Liliana M.; Ramírez-Galicia, Guillermo; Garduño‐Juárez, Ramón; Deeb, Omar; Contreras-Romo, Martha Citlalli; Quintanar‐Stephano, Andrés; Abarca-Rojano, Edgar; Correa-Basurto, José

doi:10.1111/cbdd.13005

“…In addition, when physicochemical properties or structures are expressed by numbers, we can construct a mathematical relationship, or quantitative structure-activity relationship, between the two. The resulting mathematical expression can then be used to predict the response of other chemical structures [89,90].…”

Section: Basics Of Qsar For Biopolymer Ligand Bindingmentioning

confidence: 99%

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

Garduño-Juarez,

Tovar-Anaya,

Perez-Aguilar

et al. 2024

Preprint

0

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Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its recent prominence in Bio-medical Engineering for biopolymer materials has recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: A) Computer-aided design (CAD/CAM), B) Finite Element Analysis, and C) Molecular Dynamic Simulations. This review centers on Molecular Dynamics (MD) simulations in biopolymers, analyzing structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, generating spatial trajectories for each atom, providing insights into properties like water absorption on biopolymer surfaces and interactions with solid surfaces, crucial for biomaterial assessment. This review offers readers a comprehensive overview of the diverse applications of Molecular Dynamics (MD) simulations on biopolymers. Furthermore, it emphasizes the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

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“…Since then, synthetic chemists have had limited success using the known QSAR models. Deriving multiple fully validated QSAR models with one or more readily understandable descriptors in each derived model is one way to get around this significant constraint [31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Exploring QSARs for inhibitory effect of a set of EGFR tyrosine kinase inhibitors by GA-MLR and molecular Docking simulations

Deeb,

Muhtaseb,

Shaik

2024

BJMAS

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In this study, EGFR as a target for the anticancer activity of a series of 113 inhibitors were taken from the literature. Current work aims to derive statistically robust and appropriately validated multiple QSAR models using easily interpretable molecular descriptors and molecular docking analysis. It will be simpler to identify important structural trends and how they relate to anticancer activity as a result. The MLR model has been used to suggest some novel compounds with improved activity. It has been demonstrated how the predicted compounds interact with the enzyme using the docking study. All predicted compounds were discovered to have several hydrogen bonds with the receptor and involve their bulky groups in strong steric interactions with specific places of the enzyme. The proposed compounds exhibit good pharmacokinetic properties, according to the analysis of their pharmacokinetic profiles.

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Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

Garduño-Juárez,

Tovar-Anaya,

Perez-Aguilar

et al. 2024

Polymers

1

0

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Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton’s equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

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Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies

Cited by 3 publications

References 58 publications

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

Exploring QSARs for inhibitory effect of a set of EGFR tyrosine kinase inhibitors by GA-MLR and molecular Docking simulations

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

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