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Pharmaceuticals
Special Issues
Application of Computer Simulation in Drug Design
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Application of Computer Simulation in Drug Design

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 2436

Special Issue Editors

Dr. Alessia Bono

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Guest Editor
1. Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, Viale delle Scienze, Ed. 17, I-90128 Palermo, Italy
2. Fondazione Umberto Veronesi (FUV), via Solferino 19, 20121 Milan, Italy
Interests: medicinal chemistry; computer-aided drug design; targeted cancer therapy; molecular modeling; drug development; in silico; breast cancer; design and synthesis; anticancer drug design
Prof. Antonino Lauria

E-Mail Website
Guest Editor
1. Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, Viale delle Scienze, Ed. 17, I-90128 Palermo, Italy
2. NBFC, National Biodiversity Future Center, Piazza Marina 61, 90133 Palermo, Italy
Interests: medicinal chemistry; computational approaches; computer-aided drug design; targeted cancer therapy; molecular modeling; drug development; in silico; breast cancer; design and synthesis; anticancer early drug discovery; covalent inhibition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing power of computer simulations is reshaping the landscape of modern drug discovery. From early hit identification to lead optimization, in silico methods—including molecular docking, pharmacophore modeling, molecular dynamics simulations, and AI-driven algorithms—are becoming essential components in the development of novel small-molecule inhibitors (SMIs). These approaches provide efficient, cost-effective, and mechanistically insightful strategies for identifying and optimizing new therapeutic candidates.

This Special Issue aims to showcase recent advances and innovations in the application of computational simulations to drug design, with a particular emphasis on the rational discovery and optimization of SMIs. We encourage submissions of original research and comprehensive reviews exploring diverse computational techniques applied to various disease areas, including oncology, infectious diseases, and neurodegeneration.

Topics of interest include, but are not limited to, the following: virtual screening, structure-based drug design, molecular dynamics, AI/ML-assisted drug discovery, binding free-energy calculations, drug repurposing, and integrative strategies.

Our goal is to provide an up-to-date overview of how computer simulations are transforming drug design and to inspire collaborative efforts across disciplines that accelerate the development of effective therapeutics.

We look forward to receiving your contributions.

Dr. Alessia Bono
Prof. Dr. Antonino Lauria
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • in silico
  • drug design
  • drug discovery
  • molecular docking
  • molecular dynamics simulations
  • structure-based
  • ligand-based
  • virtual screening
  • drug repurposing

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Published Papers (3 papers)

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28 pages, 7941 KB  
Article
Decoding GuaB: Machine Learning-Powered Discovery of Enzyme Inhibitors Against the Superbug Acinetobacter baumannii
by Mohammad Abdullah Aljasir and Sajjad Ahmad
Pharmaceuticals 2025, 18(12), 1842; https://doi.org/10.3390/ph18121842 - 2 Dec 2025
Viewed by 365
Abstract
Background/Objectives: GuaB, which is known as inosine 5′-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii. GuaB has emerged as a potential therapeutic target to cope with increasing antibiotic resistance. [...] Read more.
Background/Objectives: GuaB, which is known as inosine 5′-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii. GuaB has emerged as a potential therapeutic target to cope with increasing antibiotic resistance. Here, we used machine learning-based virtual screening as a verification technique to find potential inhibitors possessing different chemical scaffolds, using structure-based drug design as a discovery platform. Methods: Four machine learning models, built based on chemical fingerprint data, were trained, and the best models were used for virtual screening of the ChEMBL library, which covers 153 active molecules. Molecular dynamics (MD) simulations of 200 ns were carried out for all three compounds in order to explain conformational changes, evaluate stability, and provide validation of the docking results. Post-simulation analyses include principal component analysis (PCA), bond analysis, free-energy landscape (FEL), dynamic cross-correlation matrix (DCCM), radial distribution function (RDF), salt-bridge identification, and secondary-structure profiling, etc. Results: For molecular docking, the screened compounds were used against the GuaB protein to achieve proper docked conformation. Upon visual examination of the best-docked compounds, three leads (lead-1, lead-2, and lead-3) were found to have better interaction with the GuaB protein in comparison to the control. The mean RMSD scores between the three leads and the control were between 2.54 and 2.89 Å. In addition, the three leads as well as the control were characterized for pharmacokinetic features. All three leads met Lipinski’s Rule 5 and were thus drug-like. PCA and FEL analyses showed that lead-2 exhibited improved conformational stability, identified as deeper energy minima, whereas RDF and DCCM analyses revealed that lead-2 and lead-3 exhibited strong local structuring and concerted dynamics. In addition, lead-2 displayed a very rich hydrogen-bonding network with a total of 460 frames possessing such interactions, which is the highest among the complexes investigated here. Based on entropy calculations and the maximum entropy method of gamma–gram, lead-1 proved to be the most stable one with the lowest binding free-energy. Conclusions: This study provides an integrated machine learning-based virtual screening pipeline for the identification of new scaffolds to moderate infections associated with AMR; however, in vitro validation is still required to assess the efficacy of such compounds. Full article
(This article belongs to the Special Issue Application of Computer Simulation in Drug Design)
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33 pages, 12187 KB  
Article
A Hybrid In Silico Approach for Identifying Dual VEGFR/RAS Inhibitors as Potential Anticancer and Anti-Angiogenic Agents
by Alessia Bono, Gabriele La Monica, Federica Alamia, Dennis Tocco, Antonino Lauria and Annamaria Martorana
Pharmaceuticals 2025, 18(10), 1579; https://doi.org/10.3390/ph18101579 - 18 Oct 2025
Viewed by 638
Abstract
Background: Angiogenesis, the physiological process by which new blood vessels originate from pre-existing ones, can be triggered by tumor cells to promote the growth, survival, and progression of cancer. Malignant tumors require a constant blood supply to meet their needs for oxygen [...] Read more.
Background: Angiogenesis, the physiological process by which new blood vessels originate from pre-existing ones, can be triggered by tumor cells to promote the growth, survival, and progression of cancer. Malignant tumors require a constant blood supply to meet their needs for oxygen and nutrients, making angiogenesis a key process in tumor development. Its pathologic role is caused by the dysregulation of signaling pathways, particularly those involving VEGFR-2, a key mediator of angiogenesis, and the K-RAS G12C mutant, a promoter of VEGF expression. Given their critical involvement in tumor progression, these targets represent promising candidates for new cancer therapies. Methods and Results: In this study, we applied an in silico hybrid and hierarchical virtual screening approach to identify potential dual VEGFR-2/K-RAS G12C inhibitors with anticancer and antiangiogenic properties. To this end, we screened the National Cancer Institute (NCI) database through ADME filtering tools. The refined dataset was then submitted to the ligand-based Biotarget Predictor Tool (BPT) in a multitarget mode. Subsequently, structure-based analysis, including molecular docking studies on VEGFR and K-RAS G12C, was performed to investigate the interactions of the most promising small molecules with both targets. Conclusions: Finally, the molecular dynamics simulations suggested compound 737734 as a promising small molecule with high stability in complex with both VEGFR-2 and K-RAS G12C, highlighting its potential as a dual-target inhibitor for cancer therapy. Full article
(This article belongs to the Special Issue Application of Computer Simulation in Drug Design)
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9 pages, 476 KB  
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PROTACs and Glues: Striking Perspectives for Engineering Cancer Therapy À La Carte
by Jean-Marc Ferrero, Jocelyn Gal, Baharia Mograbi and Gérard Milano
Pharmaceuticals 2025, 18(9), 1397; https://doi.org/10.3390/ph18091397 - 17 Sep 2025
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Abstract
PROTACs are bifunctional small molecules that simultaneously bind a target protein and a component of the ubiquitin–proteasome system, thereby inducing selective degradation of the target. They represent a class of compounds capable of achieving the complete elimination of disease-relevant proteins. Molecular glues, by [...] Read more.
PROTACs are bifunctional small molecules that simultaneously bind a target protein and a component of the ubiquitin–proteasome system, thereby inducing selective degradation of the target. They represent a class of compounds capable of achieving the complete elimination of disease-relevant proteins. Molecular glues, by contrast, enhance existing surface complementarity between an E3 ligase and a target protein, promoting its ubiquitination and subsequent degradation. Both approaches are at the forefront of current efforts to overcome the long-standing challenge of undruggable tumor targets. In this context, AI-based strategies offer a powerful means to accelerate the discovery, optimization, and production of highly selective protein binders, streamlining access to potent degraders and maximizing therapeutic potential. These capabilities open new horizons for targeting a wide spectrum of previously inaccessible molecular pathways involved in cancer progression. Altogether, these advances position PROTACs and molecular glues as transformative agents for personalized oncology, particularly within the emerging paradigm of molecular tumor boards, where tailored therapeutic decisions and tumor-adapted drugs could be made rapidly accessible for a given patient. Full article
(This article belongs to the Special Issue Application of Computer Simulation in Drug Design)
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