Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Development and Application of Computational Chemistry Methods
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Development and Application of Computational Chemistry Methods

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Chemical and Molecular Sciences".

Deadline for manuscript submissions: 10 July 2025 | Viewed by 1065

Special Issue Editors

Dr. Mirko Vanzan

E-Mail Website
Guest Editor
Department of Physics, University of Milan, Via Celoria 16, 20133 Milan, Italy
Interests: computational chemistry; DFT; TDDFT; classical molecular dynamics; enhanced sampling; multiscale modeling; nanoscience; plasmonics; catalysis
Dr. Siddharth Gautam

E-Mail Website
Guest Editor
School of Earth Sciences, Ohio State University, Columbus, OH, USA
Interests: neutron scattering; molecular dynamics simulations; Monte Carlo simulations; confined fluids; neutron radiography; dielectric relaxation spectroscopy; differential scanning calorimetry; small angle scattering; porous media; diffusive motion adsorption
Dr. Marco Romanelli

E-Mail Website
Guest Editor
Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
Interests: computational chemistry; strong coupling; excited states; multiscale modeling; metal nanoparticles; nanoscience; plasmonics; polaritonics

Special Issue Information

Dear Colleagues,

Computational chemistry has emerged as a pivotal field in modern science, revolutionizing our ability to model and understand complex chemical phenomena. As computing facilities, algorithms complexity, and analysis tools continue to improve, computational techniques offer unprecedented opportunities for fundamental and applied research.

This Special Issue aims to collect the latest advancements in these approaches, highlighting recent innovations in theoretical and computational methodologies and presenting how they enhance our understanding of chemical systems and drive new experimental investigations. By featuring contributions that showcase advances in algorithms, software, and simulations, this issue wants to provide a comprehensive overview of how computational chemistry is shaping the future of scientific inquiry and technological progress.

Dr. Mirko Vanzan
Dr. Siddharth Gautam
Dr. Marco Romanelli
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

Keywords

  • computational chemistry
  • molecular modeling
  • theoretical chemistry
  • high-performance computing
  • machine learning
  • quantum computing
  • multiscale modeling
  • ab initio simulations
  • classical molecular dynamics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 3877 KiB  
Article
ProLinker–Generator: Design of a PROTAC Linker Base on a Generation Model Using Transfer and Reinforcement Learning
by Yanlin Luo, Danyang Song, Chengwei Zhang and An Su
Appl. Sci. 2025, 15(10), 5616; https://doi.org/10.3390/app15105616 - 17 May 2025
Viewed by 212
Abstract
In PROTAC molecules, the design of the linker directly affects the formation efficiency and stability of the target protein–PROTAC–E3 ligase ternary complex, making it a critical factor in determining degradation activity. However, current linker data are limited, and the accessible chemical space remains [...] Read more.
In PROTAC molecules, the design of the linker directly affects the formation efficiency and stability of the target protein–PROTAC–E3 ligase ternary complex, making it a critical factor in determining degradation activity. However, current linker data are limited, and the accessible chemical space remains narrow. The length, conformation, and chemical composition of linkers play a decisive role in drug performance, highlighting the urgent need for innovative linker design. In this study, we propose ProLinker-Generator, a GPT-based model aimed at generating novel and effective linkers. By integrating transfer learning and reinforcement learning, the model expands the chemical space of linkers and optimizes their design. During the transfer learning phase, the model achieved high scores in validity (0.989) and novelty (0.968) for the generated molecules. In the reinforcement learning phase, it further guided the generation of molecules with ideal properties within our predefined range. ProLinker-Generator demonstrates the significant potential of AI in linker design. Full article
(This article belongs to the Special Issue Development and Application of Computational Chemistry Methods)
►▼ Show Figures

Figure 1

27 pages, 7034 KiB  
Article
Computational Insights into Aluminum and Cation Placement in Clinoptilolite: Optimizing DFT Parameters for Natural Zeolites, Part I
by Lobna Saeed and Michael Fischer
Appl. Sci. 2025, 15(7), 3955; https://doi.org/10.3390/app15073955 - 3 Apr 2025
Viewed by 317
Abstract
Several experimental studies have shown that clinoptilolite zeolite is a suitable candidate for the adsorption of pharmaceuticals and related compounds. However, there is a significant lack of detailed molecular-level insights regarding how the adsorbed species interact with the zeolite surface. In this work, [...] Read more.
Several experimental studies have shown that clinoptilolite zeolite is a suitable candidate for the adsorption of pharmaceuticals and related compounds. However, there is a significant lack of detailed molecular-level insights regarding how the adsorbed species interact with the zeolite surface. In this work, we employ electronic structure calculations and propose a reliable set of input parameters within the CP2K code in the framework of dispersion-corrected density functional theory (DFT-D) to generate bulk models and study Al and cation distributions. We aim ultimately to investigate the adsorption of emerging contaminants at the clinoptilolite surfaces. Nine different exchange-correlation functionals were tested, and the results suggest that B97-D3 functional is the most robust for this system. Moreover, our results suggest that Na+ prefers the presence of Al at T2 and T3 sites but not at T1 sites and prefers being present in channel A and/or channel B. Ca2+ tends to favor being present in channel B and favors the presence of Al at T1, T2, and T3 sites. K+ prefers the smallest channel C and likes the presence of Al at T1 and T3 sites. Moreover, we found out that the optimization of the basis sets improves the coordination of extra-framework cations like Ca with the framework oxygens. Full article
(This article belongs to the Special Issue Development and Application of Computational Chemistry Methods)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop