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Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 4th Edition

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

Deadline for manuscript submissions: 16 September 2026 | Viewed by 3143

Special Issue Editors

Dr. Marco Tutone

E-Mail Website
Guest Editor
Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università degli Studi di Palermo, Via Archirafi 32, 90123 Palermo, Italy
Interests: oxidative stress; nutraceuticals; anticancer drugs; medicinal chemistry; drug design and discovery; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking; HTVS; cystic fibrosis translational readthrough inducing drugs (TRIDs)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anna Maria Almerico

E-Mail Website
Guest Editor
Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy
Interests: medicinal chemistry; molecular modeling; QSAR; pharmacophore modeling; molecular dynamics; docking
Special Issues, Collections and Topics in MDPI journals
Dr. Giulia Culletta

E-Mail Website
Guest Editor
Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, 90123 Palermo, Italy
Interests: medicinal chemistry; drug design; computational chemistry; drug repurposing; green chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After the great success of the first three editions, we are pleased to inform you that Molecules will launch the fourth edition of the Special Issue entitled “Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 4th Edition”.

The first three editions of the Special Issue can be found at the following links:

https://www.mdpi.com/journal/molecules/special_issues/comput_appr_drug_dis_des_med_chem_bio
https://www.mdpi.com/journal/molecules/special_issues/comput_approach_drug_II
https://www.mdpi.com/journal/molecules/special_issues/9I9ZP9D26K

In this latest Special Issue, we will continue to collect manuscripts concerning computational approaches that can improve the development of new drugs or the repurposing of an old drug for the treatment of new diseases.

In this light, structure-based approaches such as docking, induced-fit docking, molecular dynamics simulations, free energy calculations, reverse docking, and ligand-based approaches, such as molecular similarity fingerprints, shape methods, pharmacophore modeling, and QSAR, represent efficient tools to obtain insights in hit/lead identification and the optimization of small molecules and/or natural compounds. Moreover, computational approaches help to predict the metabolic fate of a drug candidate and to highlight the potential toxicity of the drug candidate, reducing the number of compounds to be tested. In the end, computational approaches were revealed to be of great interest in the field of nutraceuticals, allowing for the identification of the mechanisms of action. This Special Issue welcomes submissions from researchers in the field of drug discovery and design, including original research and review articles related to pharmaceutical sciences, pharmacology, chemical biology, and bioinformatics.

Papers combining both experimental and computational studies are encouraged.

Dr. Marco Tutone
Prof. Dr. Anna Maria Almerico
Dr. Giulia Culletta
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. 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

  • QSAR and 3D-QSAR
  • unbiased and biased molecular dynamics
  • pharmacophore modeling
  • reverse modeling
  • ab initio calculations
  • protein–protein interactions
  • free energy profiling
  • modeling of nucleic acids (mRNA, rRNA, tRNA)
  • molecular docking
  • virtual screening
  • multitarget approaches
  • ADMET prediction
  • similarity analysis
  • computational approaches applied to natural compounds and nutraceuticals

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Related Special Issue

Published Papers (4 papers)

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Research

18 pages, 4138 KB  
Article
Organic Sunscreens—Biological Activity from an Enzymatic Perspective
by Anna W. Sobańska, Andrzej M. Sobański and Elżbieta Brzezińska
Molecules 2026, 31(10), 1656; https://doi.org/10.3390/molecules31101656 - 14 May 2026
Viewed by 352
Abstract
Selected organic sunscreens from different chemical families were investigated in the context of their ability to inhibit butyrylcholinesterase using novel Multiple Linear Regression, Artificial Neural Network, and Support Vector Regression models based on a set of six independent variables commonly associated with compounds’ [...] Read more.
Selected organic sunscreens from different chemical families were investigated in the context of their ability to inhibit butyrylcholinesterase using novel Multiple Linear Regression, Artificial Neural Network, and Support Vector Regression models based on a set of six independent variables commonly associated with compounds’ absorption and distribution properties. It was established that the descriptors that have a particularly strong, positive influence on the ability of compounds to inhibit BChE expressed as pIC50 are the count of rotatable bonds (nRot) and lipophilicity (logD); pIC50 is negatively correlated with flexibility (Flex), fraction of sp3 carbon atoms (Fsp3), caco-2 permeability (caco2), and plasma protein binding ability (PPB). The sunscreens that are likely to be particularly strong BChE inhibitors are Ethylhexyl Triazone (ET), Diethylhexyl Butamido Triazone (DOBT), Octocrylene (OCR), and Diethylamino Hydroxybenzoyl Hexyl Benzoate (DHHB). However, it must be stressed that ET and DOBT lie outside the chemical space of the reference compounds, so predictions for these two compounds should be treated with caution. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 4th Edition)
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18 pages, 1431 KB  
Article
Zinc Affinity of Benzamide-Based Histone Deacetylase Inhibitors: A DFT Study
by Nikolay Toshev, Kristiyan Velichkov, Yordanka Uzunova, Diana Cheshmedzhieva and Todor Dudev
Molecules 2026, 31(10), 1650; https://doi.org/10.3390/molecules31101650 - 14 May 2026
Viewed by 356
Abstract
Histone deacetylase inhibitors (HDACi) are an emerging class of epigenetic anticancer drugs that exert their activity through coordination to the catalytic Zn2+ ion within the active site of histone deacetylases (HDACs). Due to the limited isoform-selectivity of hydroxamic acid-based inhibitors, benzamide-based HDACi [...] Read more.
Histone deacetylase inhibitors (HDACi) are an emerging class of epigenetic anticancer drugs that exert their activity through coordination to the catalytic Zn2+ ion within the active site of histone deacetylases (HDACs). Due to the limited isoform-selectivity of hydroxamic acid-based inhibitors, benzamide-based HDACi (BBHDACi) have been developed as subtype-selective alternatives. Clinically relevant representatives include Chidamide, Entinostat, Mocetinostat, Zabadinostat, and Tacedinaline. Although these compounds share a conserved o-aminoanilide zinc-binding group (ZBG), they differ in linker and cap region structure, raising questions regarding their intrinsic Zn2+ affinity and coordination behavior. Herein, density functional theory (DFT) calculations were performed at the B3LYP/6-311++g(d,p) level of theory combined with the PCM solvation in methanol (ε = 33) and water (ε = 78). Geometry optimization confirmed that the trans (E) isomer of Chidamide is thermodynamically preferred. Coordination studies showed that the remaining BBHDACi adopt stable geometries, with the o-aminoanilide group preferentially forming tetracoordinated complexes that are more stable than hexacoordinated ones in polar media. Interestingly, calculated substitution free energies differed by less than ± 2 kcal.mol−1, indicating nearly identical intrinsic Zn2+ affinities across the series. These results suggest that the ZBG contributes similarly to metal coordination across all BBHDACi, whereas the overall binding strength is mainly governed by interactions of the linker and cap regions rather than by the conserved zinc-binding group itself. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 4th Edition)
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21 pages, 6912 KB  
Article
Molecular Dynamics and Solvated Interaction Energy Prioritize Cannabidiol and Cannabinol as Variant-Spanning SARS-CoV-2 RBD–ACE2 Interface Blockers
by Napat Kongtaworn, Silpsiri Sinsulpsiri, Chonnikan Hanpaibool, Phornphimon Maitarad, Panupong Mahalapbutr and Thanyada Rungrotmongkol
Molecules 2026, 31(8), 1253; https://doi.org/10.3390/molecules31081253 - 10 Apr 2026
Viewed by 883
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells when the spike receptor-binding domain (RBD) engages angiotensin-converting enzyme 2 (ACE2). Cannabinoid scaffolds have recently been reported to bind S1/RBD, block spike-mediated membrane fusion, and modulate host inflammatory pathways, making them attractive candidates [...] Read more.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells when the spike receptor-binding domain (RBD) engages angiotensin-converting enzyme 2 (ACE2). Cannabinoid scaffolds have recently been reported to bind S1/RBD, block spike-mediated membrane fusion, and modulate host inflammatory pathways, making them attractive candidates for entry inhibition. Here, we applied an integrated computational pipeline to prioritize cannabis-derived compounds as interfacial blockers of the RBD–ACE2 complex across variants. Eleven phytocannabinoids were docked into the wild-type (WT) RBD–ACE2 interface, identifying three cavities, with ligands preferentially occupying pocket 1. Complexes were subjected to triplicate 200 ns all-atom molecular dynamics (MD) simulations for WT, Delta, and Omicron BA.1 RBD–ACE2. Binding energetics were quantified using molecular mechanics/generalized Born surface area (MM/GBSA) and solvated interaction energy (SIE), and per-residue contributions were analyzed together with solvent-accessible surface area (SASA) and residue interaction networks. Among all compounds, cannabidiol (CBD) and cannabinol (CBN) were the only ligands that remained stably bound in pocket 1 for all variants. CBN showed the most favorable ligand–complex binding in WT, whereas CBD preserved favorable binding in Omicron BA.1 despite reduced interface burial, indicating that van der Waals/electrostatic complementarity and solvation, rather than surface coverage alone, govern affinity. Both ligands weakened modeled RBD–ACE2 binding by perturbing hot-spot residues centered on Y505 or N501Y in RBD and E37, A387, and R393 in ACE2. Overall, our results highlight CBD and CBN as tractable, variant-spanning interface disruptors and illustrate how MD-based free-energy calculations can support computational drug discovery against evolving viral protein–protein interfaces. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 4th Edition)
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23 pages, 4916 KB  
Article
The Difluoroboranyl-Fluoroquinolone Derivative “7a” Inhibits Bacterial DNA Gyrase and Exhibits Potent Activity Against Ciprofloxacin-Resistant S. aureus In Vitro and In Vivo Using an Acute Pneumonia Model
by Luis Angel Veyna-Hurtado, Hiram Hernández-López, Denisse de Loera, Juan Manuel Vargas-Morales, Martín Muñoz-Ortega, Lorena Troncoso-Vázquez, Alondra Bocanegra-Zapata and Alberto Rafael Cervantes-Villagrana
Molecules 2026, 31(6), 1044; https://doi.org/10.3390/molecules31061044 - 20 Mar 2026
Viewed by 796
Abstract
According to the World Health Organization, antibiotic research remains insufficient, emphasizing the urgent need for new active molecules, particularly against resistant bacteria. Based on known antibacterial scaffolds, new fluoroquinolone derivatives have been synthesized by our research group, including compound 7a, a difluoroboranyl-fluoroquinolone [...] Read more.
According to the World Health Organization, antibiotic research remains insufficient, emphasizing the urgent need for new active molecules, particularly against resistant bacteria. Based on known antibacterial scaffolds, new fluoroquinolone derivatives have been synthesized by our research group, including compound 7a, a difluoroboranyl-fluoroquinolone that previously demonstrated activity against sensitive strains. Methods: The minimum inhibitory (MIC) and bactericidal (MBC) concentrations of compound 7a were determined against Staphylococcus aureus, Klebsiella pneumoniae, and Escherichia coli. The selective development of ciprofloxacin-resistant S. aureus was induced by reseeding the isolate on seven consecutive days with an antibiotic concentration that was not capable of inhibiting its development. Pharmacokinetic and toxicological properties were predicted using SwissADME, Way2Drug, and molecular docking (AutoDock Vina). In vivo toxicity was evaluated in BALB/c mice through histopathological liver and kidney analysis and serum biochemical markers. The antibacterial efficacy of 7a (80 mg/kg/day) was assessed in a murine pneumonia model induced by ciprofloxacin-resistant S. aureus. DNA gyrase inhibition was confirmed through plasmid electrophoresis assays in E. coli DH5-α cells. Results: Compound 7a exhibited both MIC and MBC values of 0.25 μg/mL, while ciprofloxacin-resistant S. aureus strains did not exhibit a detectable MIC within the concentration range tested (up to 1024 μg/mL). In silico predictions revealed favorable ADME profiles, low toxicity, and strong interaction with DNA gyrase. In vivo, 7a showed no signs of hepatotoxicity or nephrotoxicity and effectively reduced pneumonic tissue to 1.99% in infected mice. Electrophoretic assays confirmed DNA gyrase inhibition consistent with the mechanism of fluoroquinolones. Conclusions: Compound 7a evidenced activity against ciprofloxacin-resistant S. aureus in vitro and reduced infection progression in vivo. It also displays favorable drug-like properties, low predicted toxicity, and DNA gyrase inhibition. Full article
(This article belongs to the Special Issue Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics, 4th Edition)
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