Validation of an MD simulation approach for electrical field responsive micelles and their application in drug delivery

Razavi, Leila; Raissi, Heidar; Farzad, Farzaneh

doi:10.1038/s41598-023-29835-y

Cited by 13 publications

(3 citation statements)

References 62 publications

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“…All bond lengths are fixed with the LINCS (linear constraint solver) algorithm 43 . The leap-frog method with a time step of 2 fs is utilized to integrate Newton's equations of motion 44 . The cut-off distance for van der Waals (vdW) interactions is considered at 1.4 nm.…”

Section: Simulationmentioning

confidence: 99%

Significantly enhanced performance for phenol compounds removal by MOF-5 nano-composite via its surface modification

Razavi,

Raissi,

Hashemzehi

et al. 2024

npj Clean Water

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The present study is focused on the use of cubic metal-organic frameworks-5 (MOF-5) and its functionalized form in the removal of phenolic pollutants by molecular dynamics (MD) and Well-tempered metadynamics (WTMD) simulation methods. It was found that the adsorption mechanism of MOF-5s/phenolic compounds is mostly due to the van der Waals and π–π interactions. However, electrostatic and hydrogen bond (HB) interactions also play a significant role in removing phenolic pollutants by MOF-5 and its functionalized form. The results show that the fluorine functional group (F-MOF-5) increases the adsorption capacity of phenol compounds on the adsorbent surface. By functionalizing the MOF-5 with a methyl functional group (CH3-MOF-5), the adsorption strength decreases. The WTMD calculation confirmed that at the most stable state, the free energy (FE) value of system II (the most stable system in functionalized systems with –F functional group) is about −289.528 kJ mol−1. This value is ~5.781 and 35.514 kJ mol−1 more negative than the FE of the I and III systems (the most stable systems in the pristine and CH3-MOF-5/pollutant systems, respectively). Altogether, the results indicate that F-MOF-5 can be considered a more suitable adsorbent than MOF-5 and CH3-MOF-5 for phenolic pollutants removal from the environment for more assessment.

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Section: Simulationmentioning

confidence: 99%

Significantly enhanced performance for phenol compounds removal by MOF-5 nano-composite via its surface modification

Razavi,

Raissi,

Hashemzehi

et al. 2024

npj Clean Water

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“…In 2023, Razavi et al applied AAMD and used different analyses of the MD trajectory, such as center-of-mass (COM) distance, solvent-accessible surface area (SASA), radial distribution function (RDF), and amount of hydrogen bonds (HB), to understand the self-assembly and adsorption capacity of a supramolecular micelle. [62] The host-guest interaction of two end-capped homopolymers, polystyrene-β-cyclodextrin (PS-β-CD) and polyethylene oxide-ferrocene (PE-FE), forms a block copolymer (PS-β-CD/PE-FE) that can aggregate to form the supramolecular micelles. The authors have used the abovementioned trajectory analysis, as well as energetic parameters, to understand (i) the formation, (ii) electric field (EF) response, and (iii) drug delivery capacity of this supramolecular micelle.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

“…In 2023, Razavi et al. applied AAMD and used different analyses of the MD trajectory, such as center‐of‐mass (COM) distance, solvent‐accessible surface area (SASA), radial distribution function (RDF), and amount of hydrogen bonds (HB), to understand the self‐assembly and adsorption capacity of a supramolecular micelle [62] . The host‐guest interaction of two end‐capped homopolymers, polystyrene‐ β ‐cyclodextrin (PS‐ β ‐CD) and polyethylene oxide‐ferrocene (PE‐FE), forms a block copolymer (PS‐ β ‐CD/PE‐FE) that can aggregate to form the supramolecular micelles.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

Supramolecular Chemistry: Exploring the Use of Electronic Structure, Molecular Dynamics, and Machine Learning Approaches

Colaço,

Glitz,

Jacobs

et al. 2024

Eur J Org Chem

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This review aims to highlight the role that computational chemistry has played in advancing the supramolecular chemistry field. We demonstrated recent uses of computational methodologies to elucidate noncovalent interactions in various processes occurring in supramolecular systems. We also emphasized the contributions of these techniques to studying reactions within confined space, showing how computational methodologies help clarify the effects of reactivity and conformational locking. Furthermore, we underscore the utilization of Molecular Dynamics (MD) in elucidating dynamical processes, understanding temperature and pressure effects, and exploring conformational space within supramolecular chemistry. Finally, we highlight the impact that the age of machine learning has on computational chemistry, showing how these universal approximators can enhance existing methods, predict properties, and efficiently explore the chemical space encompassed by these complex systems.

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Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

Gao,

Ciura,

et al. 2024

Advanced Materials

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The pioneering work on liposomes in the 1960s and subsequent research in controlled drug release systems significantly advances the development of nanocarriers (NCs) for drug delivery. This field is evolved to include a diverse array of nanocarriers such as liposomes, polymeric nanoparticles, dendrimers, and more, each tailored to specific therapeutic applications. Despite significant achievements, the clinical translation of nanocarriers is limited, primarily due to the low efficiency of drug delivery and an incomplete understanding of nanocarrier interactions with biological systems. Addressing these challenges requires interdisciplinary collaboration and a deep understanding of the nano‐bio interface. To enhance nanocarrier design, scientists employ both physics‐based and data‐driven models. Physics‐based models provide detailed insights into chemical reactions and interactions at atomic and molecular scales, while data‐driven models leverage machine learning to analyze large datasets and uncover hidden mechanisms. The integration of these models presents challenges such as harmonizing different modeling approaches and ensuring model validation and generalization across biological systems. However, this integration is crucial for developing effective and targeted nanocarrier systems. By integrating these approaches with enhanced data infrastructure, explainable AI, computational advances, and machine learning potentials, researchers can develop innovative nanomedicine solutions, ultimately improving therapeutic outcomes.

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Validation of an MD simulation approach for electrical field responsive micelles and their application in drug delivery

Cited by 13 publications

References 62 publications

Significantly enhanced performance for phenol compounds removal by MOF-5 nano-composite via its surface modification

Significantly enhanced performance for phenol compounds removal by MOF-5 nano-composite via its surface modification

Supramolecular Chemistry: Exploring the Use of Electronic Structure, Molecular Dynamics, and Machine Learning Approaches

Toward the Integration of Machine Learning and Molecular Modeling for Designing Drug Delivery Nanocarriers

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