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Viruses
Special Issues
Computational Drug Discovery for Viral Infections
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Computational Drug Discovery for Viral Infections

A special issue of Viruses (ISSN 1999-4915). This special issue belongs to the section "Viral Immunology, Vaccines, and Antivirals".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4128

Special Issue Editor

Dr. George D. Geromichalos

E-Mail Website
Guest Editor
Department of General and Inorganic Chemistry, Faculty of Chemistry, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
Interests: pharmacology and medicinal chemistry; targeted rational computer-aided drug design and discovery; in silico computational chemistry techniques; molecular modeling; similarity and pharmacophore searches; cheminformatics; structure-based and ligand-based virtual screening drug design; de novo drug design; signal transduction pathways; function of target proteins

Special Issue Information

Dear Colleagues,

Today, the demand for antivirals against emerging viruses threats to human health has increased due to the possibility of future outbreaks and epidemics. Since the traditional drug development process takes around a decade to produce, validate, and approve each new medication, novel therapeutic strategies are needed to integrate data compiled by a wide range of scientists to inform new therapies. Understanding the molecular basis of drug action and exploring the chemical interactions involved in the complex processes of drug delivery and reaction with a variety of biological molecules are among the most important goals of contemporary drug design.

Today’s viral treatment arsenal largely comprises drugs developed by using computational drug discovery techniques. The integration of structural biology approaches in combination with modeling and modern computational drug discovery tools may play a crucial role in establishing novel antiviral therapies.

Due to the current urgent need for novel antiviral agents with potent inhibitory activities, new insights into molecular mechanisms related to drug development are important in the viral infection treatment process.

The development of targeted therapy for viral infection diseases involves the design of novel drug molecules to target a wide range of different proteins involved in the pathogenesis of the related diseases. Additionally, drug repurposing offers several advantages over discovery from scratch in the antiviral strategies approach. In silico techniques for the discovery of new antiviral drugs before their validation through in vitro assays could accelerate the discovery of effective antiviral agents and therapeutic strategies.

This Special Issue aims to present the most recent innovative therapeutic strategies and findings regarding research on the discovery of new interventions targeting the elucidation of the molecular mechanisms underlying viral infection diseases.

Manuscript Details:

In this Special Issue, we welcome the submission of original research and review papers covering the recent advancements in and current understanding of computational technology aspects in the development of new antiviral therapeutic drugs.

Both original research and review articles presenting outstanding scientific contributions are welcomed. This Special Issue aims to include, but is not limited to, the following research areas:

  • Computational drug design approaches;
  • Structure-based and ligand-based virtual screening;
  • Pharmacophore modeling;
  • Drug likelihood and toxicity;
  • SAR/QSAR modeling;
  • ADMET optimization;
  • Chimeric bifunctional molecules;
  • Multitarget-directed compounds;
  • Drug repurposing.

Dr. George D. Geromichalos
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. Viruses 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 2600 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 drug design
  • structure-based drug design
  • ligand-based drug design
  • pharmacophore modeling
  • therapeutic targets
  • drug repurposing strategy
  • virtual screening approach
  • SAR/QSAR modeling
  • novel antiviral compounds
  • theoretical modeling
  • molecular pathways
  • molecular medicine
  • molecular mechanisms

Published Papers (3 papers)

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Research

17 pages, 3127 KiB  
Article
Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing
by Sakshi Gautam, Anamika Thakur, Akanksha Rajput and Manoj Kumar
Viruses 2024, 16(1), 45; https://doi.org/10.3390/v16010045 - 27 Dec 2023
Cited by 1 | Viewed by 1315
Abstract
Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the “Anti-Dengue” algorithm that predicts dengue virus inhibitors using a quantitative structure–activity relationship (QSAR) and MLTs. Using the “DrugRepV” database, we [...] Read more.
Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the “Anti-Dengue” algorithm that predicts dengue virus inhibitors using a quantitative structure–activity relationship (QSAR) and MLTs. Using the “DrugRepV” database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model’s reliability and robustness were assessed using William’s plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. “Anti-Dengue” may be beneficial in accelerating antiviral drug development against the dengue virus. Full article
(This article belongs to the Special Issue Computational Drug Discovery for Viral Infections)
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15 pages, 1915 KiB  
Article
Computational Analysis of CD46 Protein Interaction with SARS-CoV-2 Structural Proteins: Elucidating a Putative Viral Entry Mechanism into Human Cells
by Pavel Vassiliev, Evgenii Gusev, Maria Komelkova, Andrey Kochetkov, Maria Dobrynina and Alexey Sarapultsev
Viruses 2023, 15(12), 2297; https://doi.org/10.3390/v15122297 - 23 Nov 2023
Viewed by 834
Abstract
This study examines an unexplored aspect of SARS-CoV-2 entry into host cells, which is widely understood to occur via the viral spike (S) protein’s interaction with human ACE2-associated proteins. While vaccines and inhibitors targeting this mechanism are in use, they may not offer [...] Read more.
This study examines an unexplored aspect of SARS-CoV-2 entry into host cells, which is widely understood to occur via the viral spike (S) protein’s interaction with human ACE2-associated proteins. While vaccines and inhibitors targeting this mechanism are in use, they may not offer complete protection against reinfection. Hence, we investigate putative receptors and their cofactors. Specifically, we propose CD46, a human membrane cofactor protein, as a potential putative receptor and explore its role in cellular invasion, acting possibly as a cofactor with other viral structural proteins. Employing computational techniques, we created full-size 3D models of human CD46 and four key SARS-CoV-2 structural proteins—EP, MP, NP, and SP. We further developed 3D models of CD46 complexes interacting with these proteins. The primary aim is to pinpoint the likely interaction domains between CD46 and these structural proteins to facilitate the identification of molecules that can block these interactions, thus offering a foundation for novel pharmacological treatments for SARS-CoV-2 infection. Full article
(This article belongs to the Special Issue Computational Drug Discovery for Viral Infections)
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25 pages, 48090 KiB  
Article
Covalent Inhibitors from Saudi Medicinal Plants Target RNA-Dependent RNA Polymerase (RdRp) of SARS-CoV-2
by Ahmed H. Bakheit, Quaiser Saquib, Sarfaraz Ahmed, Sabiha M. Ansari, Abdullah M. Al-Salem and Abdulaziz A. Al-Khedhairy
Viruses 2023, 15(11), 2175; https://doi.org/10.3390/v15112175 - 30 Oct 2023
Viewed by 1546
Abstract
COVID-19, a disease caused by SARS-CoV-2, has caused a huge loss of human life, and the number of deaths is still continuing. Despite the lack of repurposed drugs and vaccines, the search for potential small molecules to inhibit SARS-CoV-2 is in demand. Hence, [...] Read more.
COVID-19, a disease caused by SARS-CoV-2, has caused a huge loss of human life, and the number of deaths is still continuing. Despite the lack of repurposed drugs and vaccines, the search for potential small molecules to inhibit SARS-CoV-2 is in demand. Hence, we relied on the drug-like characters of ten phytochemicals (compounds 1–10) that were previously isolated and purified by our research team from Saudi medicinal plants. We computationally evaluated the inhibition of RNA-dependent RNA polymerase (RdRp) by compounds 1–10. Non-covalent (reversible) docking of compounds 1–10 with RdRp led to the formation of a hydrogen bond with template primer nucleotides (A and U) and key amino acid residues (ASP623, LYS545, ARG555, ASN691, SER682, and ARG553) in its active pocket. Covalent (irreversible) docking revealed that compounds 7, 8, and 9 exhibited their irreversible nature of binding with CYS813, a crucial amino acid in the palm domain of RdRP. Molecular dynamic (MD) simulation analysis by RMSD, RMSF, and Rg parameters affirmed that RdRP complexes with compounds 7, 8, and 9 were stable and showed less deviation. Our data provide novel information on compounds 7, 8, and 9 that demonstrated their non-nucleoside and irreversible interaction capabilities to inhibit RdRp and shed new scaffolds as antivirals against SARS-CoV-2. Full article
(This article belongs to the Special Issue Computational Drug Discovery for Viral Infections)
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