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Processes
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Application of Computational Techniques in Analytical Chemistry and Molecular Systems
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Application of Computational Techniques in Analytical Chemistry and Molecular Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 3876

Special Issue Editor

Dr. Xue Yong

E-Mail Website
Guest Editor
Department of Chemistry, The University of Sheffield, Sheffield S3 7HF, UK
Interests: molecular dynamics simulations; DFT; carbon dots; CO2 reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the dynamic landscape of analytical chemistry and molecular systems research, the integration of computational techniques has ushered in a new era of innovation and discovery. Computational modeling provides detailed information on electronic structures and serves as a tool to understand how electronic structures respond to photons, which reflect the macroscopic properties of molecular systems. These properties can lead to various application fields, such as solar cells and catalysts. We invite researchers and experts from across the globe to contribute their insights and findings to a Special Issue dedicated to the "Application of Computational Techniques in Analytical Chemistry and Molecular Systems."

The aim of this Special Issue is to provide a platform for the exchange of knowledge, ideas, and breakthroughs at the intersection of computational science and analytical chemistry. We seek abstract submissions that encompass a wide spectrum of topics, including but not limited to the following:

  1. Molecular modeling and simulations for drug discovery and development.
  2. Computational methods for the analysis of complex biomolecular systems.
  3. Machine learning and artificial intelligence applications in analytical chemistry.
  4. Quantum chemistry approaches to elucidate molecular behavior.
  5. Big data analytics for high-throughput chemical analysis.
  6. Computational advances in spectroscopy and mass spectrometry.
  7. Chemoinformatics and bioinformatics in chemical analysis.
  8. Computational methods for material characterization and nanotechnology.
  9. Predictive modeling and data-driven approaches in analytical instrumentation.
  10. Innovative software tools and algorithms for analytical chemistry.

Dr. Xue Yong
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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16 pages, 3431 KiB  
Article
Radioiodinated Anastrozole and Epirubicin for HER2-Targeted Cancer Therapy: Molecular Docking and Dynamics Insights with Implications for Nuclear Imaging
by Mazen Abdulrahman Binmujlli
Processes 2024, 12(8), 1659; https://doi.org/10.3390/pr12081659 - 7 Aug 2024
Viewed by 1083
Abstract
This study evaluates radioiodinated anastrozole ([125I]anastrozole) and epirubicin ([125I]epirubicin) for HER2-targeted cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment of HER2-positive cancers. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds’ binding affinities and mechanisms [...] Read more.
This study evaluates radioiodinated anastrozole ([125I]anastrozole) and epirubicin ([125I]epirubicin) for HER2-targeted cancer therapy, utilizing radiopharmaceutical therapy (RPT) for personalized treatment of HER2-positive cancers. Through molecular docking and dynamics simulations (200 ns), it investigates these compounds’ binding affinities and mechanisms to the HER2 receptor compared to lapatinib, a known HER2 inhibitor. Molecular docking studies identified [125I]epirubicin with the highest ΔGbind (−10.92 kcal/mol) compared to lapatinib (−10.65 kcal/mol) and [125I]anastrozole (−9.65 kcal/mol). However, these differences were not statistically significant. Further molecular dynamics (MD) simulations are required to better understand the implications of these findings on the therapeutic potential of the compounds. MD simulations affirmed a stable interaction with the HER2 receptor, indicated by an average RMSD of 4.51 Å for [125I]epirubicin. RMSF analysis pointed to significant flexibility at key receptor regions, enhancing the inhibitory action against HER2. The [125I]epirubicin complex maintained an average of four H-bonds, indicating strong and stable interactions. The average Rg values for [125I]anastrozole and [125I]epirubicin complexes suggest a modest increase in structural flexibility without compromising protein compactness, reflecting their potential to induce necessary conformational changes in the HER2 receptor function. These analyses reveal enhanced flexibility and specific receptor region interactions, suggesting adaptability in binding, which could augment the inhibitory action against HER2. MM-PBSA calculations indicate the potential of these radioiodinated compounds as HER2 inhibitors. Notably, [125I]epirubicin exhibited a free binding energy of −65.81 ± 0.12 kJ/mol, which is comparable to lapatinib at −64.05 ± 0.11 kJ/mol and more favorable than [125I]anastrozole at −57.18 ± 0.12 kJ/mol. The results suggest electrostatic interactions as a major contributor to the binding affinity. The computational analysis underscores that [125I]anastrozole and [125I]epirubicin may have a promising role as HER2 inhibitors, especially [125I]epirubicin due to its high binding affinity and dynamic receptor interactions. These findings, supported by molecular docking scores and MM-PBSA binding energies, advocate for their potential superior inhibitory capability against the HER2 receptor. To validate these computational predictions and evaluate the therapeutic potential of these compounds for HER2-targeted cancer therapy, it is essential to conduct empirical validation through both in vitro and in vivo studies. Full article
(This article belongs to the Special Issue Application of Computational Techniques in Analytical Chemistry and Molecular Systems)
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18 pages, 3485 KiB  
Article
Computational Insight of Oleracone L, Portulacatone B, and Portulacatal from Portulaca oleracea L. as Potential Anticholinesterase Inhibitors for Alzheimer’s
by Shifaa O. Alshammari
Processes 2024, 12(7), 1456; https://doi.org/10.3390/pr12071456 - 12 Jul 2024
Cited by 2 | Viewed by 811
Abstract
Alzheimer’s disease, characterized by a decline in cognitive functions, is frequently associated with decreased levels of acetylcholine due to the overactivity of acetylcholinesterase (AChE). Inhibiting AChE has been a key therapeutic strategy in treating Alzheimer’s disease, yet the search for effective inhibitors, particularly [...] Read more.
Alzheimer’s disease, characterized by a decline in cognitive functions, is frequently associated with decreased levels of acetylcholine due to the overactivity of acetylcholinesterase (AChE). Inhibiting AChE has been a key therapeutic strategy in treating Alzheimer’s disease, yet the search for effective inhibitors, particularly from natural sources, continues due to their potential for fewer side effects. In this context, three new alkaloids—oleracone L, portulacatone B, and portulacatal—extracted from Portulaca oleracea L., have recently shown promising anticholinesterase activity in vitro. However, no experimental or computational studies have yet explored their binding potential. This study represents the first comprehensive in silico analysis of these compounds, employing ADME prediction, molecular docking, molecular dynamics simulations, and MM-PBSA calculations to assess their therapeutic potential. The drug-likeness was evaluated based on Lipinski, Pfizer, Golden Triangle, and GSK rules, with all three alkaloids meeting these criteria. The ADME profiles suggested that these alkaloids can effectively cross the blood–brain barrier, a critical requirement for Alzheimer’s treatment. Molecular docking studies revealed that oleracone L had the highest binding affinity (−10.75 kcal/mol) towards AChE, followed by portulacatal and portulacatone B, demonstrating significant interactions with crucial enzyme residues. Molecular dynamics simulations over 200 ns confirmed the stability of these interactions, with RMSD values below 2 Å for all complexes, indicating stable binding throughout the simulation period. RMSF and the radius of gyration analyses further corroborated the minimal impact of these alkaloids on the enzyme’s overall flexibility and compactness. Moreover, MM-PBSA calculations provided additional support for the binding efficacy, showing that oleracone L, with the most favorable binding energy, could be a superior inhibitor, potentially due to its stronger and more consistent hydrogen bonding and favorable electrostatic interactions compared to the other studied alkaloids. These computational findings highlight the binding efficiency and potential therapeutic viability of these alkaloids as AChE inhibitors, suggesting they could be promising candidates for Alzheimer’s disease treatment. The study underscores the importance of further validation through in vitro and in vivo experiments to confirm these predictions. Full article
(This article belongs to the Special Issue Application of Computational Techniques in Analytical Chemistry and Molecular Systems)
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18 pages, 3246 KiB  
Article
Mathematical Models for Estimating Diffusion Coefficients in Concentrated Polymer Solutions from Experimental Data
by Adriana Mariana Asoltanei, Eugenia Teodora Iacob-Tudose, Marius Sebastian Secula and Ioan Mamaliga
Processes 2024, 12(6), 1266; https://doi.org/10.3390/pr12061266 - 19 Jun 2024
Cited by 1 | Viewed by 1483
Abstract
Diffusion processes in operations involving polymeric materials are of significant interest. Determining experimental values for diffusion coefficients is often challenging. Estimating these coefficients in concentrated polymer solution, polymer films, and membranes relies on experimental tests where the polymer is brought into contact with [...] Read more.
Diffusion processes in operations involving polymeric materials are of significant interest. Determining experimental values for diffusion coefficients is often challenging. Estimating these coefficients in concentrated polymer solution, polymer films, and membranes relies on experimental tests where the polymer is brought into contact with certain components/solvents. The diffusion coefficient values depend on the diffusion type, which is affected mainly by the nature of the polymer, concentration, and temperature. The literature presents an extensive amount of information regarding the diffusion phenomenon. This paper makes a particular contribution by showing how experimental data obtained from different applications can be processed to determine diffusion coefficients. The manuscript addresses some aspects regarding solvent diffusion in polymers, and illustrates how to determine the diffusion coefficients from experimental data. For specific cases of diffusion, several models for the predictive estimation of diffusion coefficients are also presented. Polymer–solvent systems such as polyvinyl alcohol (PVA)–water, cellulose acetate (CA)–tetrahydrofuran (THF) and cellulose triacetate (CTA)–dichloromethane (DCM) are investigated, with their diffusion mechanisms influenced by changes in structure caused by variations in concentration and temperature. The experimental data obtained through a gravitational technique allow for the highlighting of the diffusion mechanism and the selection of an appropriate mathematical model. A change in the structure of the polymer during the experiment leads to diffusion anomalies. Modeling the experimental data yielded diffusion coefficient values that vary based on the type of system investigated, composition and temperature. Thus, in the case of the CTA-DCM system, the diffusion coefficient at 303 K, at various concentration values, is in the range of 4.5 and 8·10−11 m2/s; for the PVA-H2O system, D = 4.1·10−12 m2/s at 303 K, and D = 6.5·10−12 m2/s at 333 K; while for the CA-THF system, the solvent–polymer diffusion coefficient has a value of 2.5∙10−12 m2/s at 303 K, and D = 1.75∙10−11 m2/s at 323 K. Mathematical models can be useful in studies regarding the drying of polymer films with complex structures, providing knowledge for designing or selecting suitable equipment. Full article
(This article belongs to the Special Issue Application of Computational Techniques in Analytical Chemistry and Molecular Systems)
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