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Molecular Dynamics Inc molecular dynamics simulations
Molecular Dynamics Simulations, supplied by Molecular Dynamics Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/molecular+dynamics+simulations/dynamics+molecular+simulations/pm42311206-41-1-1
Average 86 stars, based on 1 article reviews
molecular dynamics simulations - by Bioz Stars, 2026-09
86/100 stars

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Article Title: Probing Composition Effects on the Intrinsic Energetics of Proton Transfer in Binary (HCl) m (H 2 O) n Clusters.
Article Snippet: S. Sugawara, T. Yoshikawa, T. Takayanagi, and M. Tachikawa, “Theoretical Study on Mechanisms of Structural Rearrangement and Ionic Dissociation in the HCl(H2O)4 Cluster with Path-Integral Molecular Dynamics Simulations,” Chemical Physics Letters 501 (2011): 238–244.

Article Title: Selenium-Containing 1,4-Naphthoquinone Derivatives Trigger Reactive Oxygen Species and Apoptosis in Triple-Negative Breast Cancer Cells.
Article Snippet: Additionally, Molecular Dynamics Simulations (MD) were performed to investigate the molecular recognition of the ligands by topoisomerase II and evaluate their potential as enzyme inhibitors.

Article Title: The co-assembled hesperidin-emodin nanospindles for synergistic enhancement of regenerative repair in MRSA-infected incisions
Article Snippet: Pathogen colonization, uncontrolled inflammation, and impaired tissue repair impede infected wound healing.. Among natural products, hesperidin (HSD) and emodin (EMO) showed wound-healing and antibacterial potential, respectively.. However, their poor solubility and low bioavailability significantly restrict the therapeutic efficacy in infected wounds.

Article Title: Structure-Property Relationships in Symmetrical Bolaamphiphilic Dehydrodipeptides: Self-Assembled Injectable Hydrogels for Anticancer Drug Delivery.
Article Snippet: PACKMOL: A Package for Building Initial Configurations for Molecular Dynamics Simulations.

Binding Assay:

Article Title: Synthesis, biological evaluation and molecular docking studies of N -propylsulfonyl indole-linked hydrazinecarbothioamides as selective ecto-5′-nucleotidase and NTPDase inhibitors
Article Snippet: .. Molecular dynamics simulations (500 ns) of the 5n – and 5o–e-5′NT complexes confirmed the stability of the binding interactions. ..

Article Title: Protein fibril-based delivery systems for bioactive substances: Structural design, functional regulation, and food applications.
Article Snippet: Protein fibrils (PFs) are β-sheet-rich supramolecular assemblies with high aspect ratios, tunable surface chemistry, and multiscale assembly behavior, making them promising food-grade carriers for bioactive compounds.. However, their delivery performance depends not simply on increasing fibril order or structural stability, but on balancing carrier robustness with molecular binding and release behavior.. This review examines the regulatory mechanisms of PFs and the roles of key structural features in governing their physicochemical and biological activities, treating PFs as foundational templates rather than direct raw materials.

Article Title: AI-driven fungicide design: From target identification to field application
Article Snippet: Mutant target structure generation , Structure prediction algorithms (e.g., AlphaFold2) and structural refinement tools (energy minimization) , Generate high-fidelity 3D structures of mutant target proteins based on identified hotspots and optimize structural stability to match physiological conditions , 3D models of wild-type and mutant target proteins. .. Binding affinity change calculation , Molecular dynamics simulations and binding free energy calculation tools (e.g., MM-GBSA/MM-PBSA) , Simulate ligand–target binding dynamics for both wild-type and mutant complexes and quantify differences in binding affinity induced by mutations , Binding free energy changes between wild-type/mutant targets and lead compounds. .. Resistance risk classification , Machine learning models (e.g., random forest and support vector machine) and resistance risk scoring systems , Classify lead compounds into risk tiers (low/medium/high) based on binding affinity changes and population mutation potential and prioritize candidates with low resistance tendency , Resistance risk classification of lead compounds (low/medium/high risk).

Functional Assay:

Article Title: Protein fibril-based delivery systems for bioactive substances: Structural design, functional regulation, and food applications.
Article Snippet: Protein fibrils (PFs) are β-sheet-rich supramolecular assemblies with high aspect ratios, tunable surface chemistry, and multiscale assembly behavior, making them promising food-grade carriers for bioactive compounds.. However, their delivery performance depends not simply on increasing fibril order or structural stability, but on balancing carrier robustness with molecular binding and release behavior.. This review examines the regulatory mechanisms of PFs and the roles of key structural features in governing their physicochemical and biological activities, treating PFs as foundational templates rather than direct raw materials.

Mutagenesis:

Article Title: AI-driven fungicide design: From target identification to field application
Article Snippet: Mutant target structure generation , Structure prediction algorithms (e.g., AlphaFold2) and structural refinement tools (energy minimization) , Generate high-fidelity 3D structures of mutant target proteins based on identified hotspots and optimize structural stability to match physiological conditions , 3D models of wild-type and mutant target proteins. .. Binding affinity change calculation , Molecular dynamics simulations and binding free energy calculation tools (e.g., MM-GBSA/MM-PBSA) , Simulate ligand–target binding dynamics for both wild-type and mutant complexes and quantify differences in binding affinity induced by mutations , Binding free energy changes between wild-type/mutant targets and lead compounds. .. Resistance risk classification , Machine learning models (e.g., random forest and support vector machine) and resistance risk scoring systems , Classify lead compounds into risk tiers (low/medium/high) based on binding affinity changes and population mutation potential and prioritize candidates with low resistance tendency , Resistance risk classification of lead compounds (low/medium/high risk).



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