Special Issue "Computer-Aided Drug Design and Drug Discovery"

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 1378

Special Issue Editors

Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania
Interests: pharmacology; drug discovery; drug repurposing; virtual screening; small molecule drugs; neurodegenerative diseases; TRP channels
Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, 020956 Bucharest, Romania
Interests: drug design; computer-assisted drug design studies; heterocyclic compounds; design and synthesis of new anticancer agents; design and synthesis of new antimicrobial compounds; studies and structural analysis; isolation and analysis of natural compounds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer-aided methods play a crucial role in every stage of drug discovery and development, enabling a more efficient and cost-effective approach. The integration of computational methods with experimental approaches has become indispensable in modern drug discovery. At a time when advancements in technology are revolutionizing the pharmaceutical industry, this Special Issue aims to highlight the latest developments in computer-aided drug design and drug discovery. The iterative process of designing, synthesizing, and testing new compounds can lead to the identification of promising drug leads. This Special Issue will provide a platform to showcase cutting-edge research, innovative methodologies, and breakthroughs that leverage computational approaches in pharmaceutical research. It will explore various aspects, including but not limited to: 

  • Novel computational techniques and algorithms in drug design;
  • Artificial intelligence and machine learning in drug discovery;
  • Molecular modeling and simulation for drug development;
  • Virtual screening and high-throughput screening methods;
  • Structure-based drug design and ligand–receptor interactions;
  • CADD in pharmacokinetics (ADME prediction);
  • Target and off-target identification;
  • Repurposing candidate prioritization;
  • Optimization strategies for lead compounds;
  • Predictive modeling and toxicology assessments;
  • Case studies and successful applications of computer-aided drug design.

We invite you to contribute to this Special Issue by submitting your original research, review articles, or perspectives that encapsulate your expertise and insights. Your contribution would provide readers with valuable knowledge and inspire further advancements in the field.

Dr. Dragos Paul Mihai
Prof. Dr. George Mihai Nitulescu
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. Pharmaceuticals 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 2900 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.


  • chemoinformatics
  • bioinformatics
  • molecular modeling
  • virtual screening
  • molecular docking
  • quantitative structure–activity relationship (QSAR)
  • pharmacophore modeling
  • target identification
  • repurposing
  • ADME-T prediction

Published Papers (1 paper)

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26 pages, 15164 KiB  
Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies
Pharmaceuticals 2023, 16(10), 1479; https://doi.org/10.3390/ph16101479 - 17 Oct 2023
Viewed by 856
The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. [...] Read more.
The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand–protein binding properties and analyze the protein–protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug–protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

In-silico design of metabolically stable SafD-binding peptidomimetics for pilus biogenesis suppression in the prevention of Salmonella-induced abdominal infections

Priyanka Samanta 1, * and Sourav Ghorai 2, *

1Department of BioMolecular Sciences, School of Pharmacy, University of Mississippi, University, MS, USA, 38677-1848; ORCID: 0000-0001-9127-5816

263 Lionel Avenue, Apt E, Waltham, MA, USA, 02452; ORCID: 0000-0001-8093-5655

*Correspondence: psamanta@go.olemiss.edu; Tel.: +1-662-915-1853 (P.S.); sghora92@gmail.com (S.G.)


Clinical isolates of Salmonella enterica contain Saf pili that establish bacterial attachment with the human epithelium which are a common cause of several abdominal complications. Saf pili undergo a chaperone-usher pathway assembly mechanism to generate its host-recognizing functional form, SafDAA. Preventing the biogenesis of the pili by targeting the SafD and SafA subunits polymerization will prevent host recognition. In this study, virtual mutagenesis and protein–peptide interaction studies have led to the design of peptidomimetics that exhibit enhanced binding with SafD. Molecular dynamics simulations and binding free energy calculations identified key pairwise interactions between the designed peptidomimetics and SafD. In silico ADMET machine-learning models were used to predict metabolic hotspots that helped in the design of metabolically stable peptidomimetics. In addition, a library of 200 peptidomimetics that are predicted to bind strongly with SafD is prepared which can serve as an excellent resource for the discovery of novel SafD-binding peptidomimetics. This work provided new insights into the design of novel anti-virulence therapies targeting Salmonella enterica.

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