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Molecular Docking in Drug Design and Development

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 800

Special Issue Editors


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Guest Editor
Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, China
Interests: molecular docking; molecular dynamics simulation; drug discovery; QSAR; virtual screening

E-Mail Website
Guest Editor
Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, China
Interests: deep learning; molecular dynamics simulation; drug discovery; molecular docking; drug–target interaction

Special Issue Information

Dear Colleagues,

This Special Issue delves into the pivotal role of molecular docking in advancing drug discovery and design. Motivated by the accelerating pace of pharmaceutical innovation, this Special Issue aims to explore cutting-edge methodologies and applications within the realm of molecular docking.

The main topic of this Special Issue revolves around the utilization of molecular docking techniques to predict and analyze the interactions between small molecules and their target proteins. It covers a wide range of areas, including but not limited to virtual screening, lead optimization, binding affinity prediction, structure-based drug design, and protein–ligand interactions.

From exploring binding mechanisms to assessing the efficacy of ligand–protein interactions, this Special Issue seeks to provide a comprehensive overview of the latest developments in this critical field. Submissions to this Special Issue can include original research articles, reviews, and methodological papers that contribute to the understanding and advancement of molecular docking in drug design and development. Computational studies, algorithm developments, case studies, and experimental validations are all welcome. The scope also encompasses the integration of molecular docking with other computational techniques, such as molecular dynamics simulations, machine learning, and data mining, to enhance the accuracy and efficiency of drug discovery processes.

Dr. Lei Xu
Dr. Liangxu Xie
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. Molecules 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 2700 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

  • molecular docking
  • drug design
  • virtual screening
  • lead optimization
  • binding affinity prediction
  • structure-based drug design
  • computational drug discovery

Published Papers (1 paper)

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Research

25 pages, 14804 KiB  
Article
Exploring the Therapeutic Potential of Petiveria alliacea L. Phytochemicals: A Computational Study on Inhibiting SARS-CoV-2’s Main Protease (Mpro)
by Md. Ahad Ali, Humaira Sheikh, Muhammad Yaseen, Md Omar Faruqe, Ihsan Ullah, Neeraj Kumar, Mashooq Ahmad Bhat and Md. Nurul Haque Mollah
Molecules 2024, 29(11), 2524; https://doi.org/10.3390/molecules29112524 - 27 May 2024
Viewed by 522
Abstract
The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs [...] Read more.
The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of −8.9, −8.7 and −8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials. Full article
(This article belongs to the Special Issue Molecular Docking in Drug Design and Development)
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