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Computationally Guided Medicinal Chemistry: Molecular Modeling, Scaffold Design, and Multi-Target Drug Discovery

A Special Issue of Molecules (ISSN 1420-3049) belonging to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 1627

Editors


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Guest Editor
Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, Athens, Greece
Interests: rational drug development; molecular simulations; advanced methods for solvation mapping; natural products; biophysics; protein kinases; epigenetics
Special Issues, Collections and Topics in MDPI journals

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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
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, Viale delle Scienze, Ed. 17, I-90128 Palermo, Italy
Interests: natural and synthetic bioactive small molecules; computer-aided drug design; covalent inhibition; vectorial chemistry; antibiotic and anticancer therapies
Special Issues, Collections and Topics in MDPI journals

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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. 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 integration of computational approaches into medicinal chemistry is reshaping the discovery and development of new therapeutic agents. Molecular modeling, chemoinformatics, and artificial intelligence now play a central role in guiding rational drug design. Their impact is particularly significant when computational predictions are integrated with experimental validation; however, advanced in silico methodologies alone are increasingly capable of providing deep mechanistic and predictive insights. 

This Special Issue aims to highlight recent advances in computational medicinal chemistry, with a primary focus on innovative in silico approaches applied to drug discovery and optimization. Topics of interest include, but are not limited to, structure- and ligand-based drug design, virtual screening, molecular dynamics simulations, pharmacophore modeling, QSAR and machine learning approaches, scaffold design, binding free-energy calculations, drug repurposing, and polypharmacology.

We welcome original research articles, reviews, and overview papers that showcase state-of-the-art computational strategies. Contributions combining computational studies with chemical synthesis and/or biological evaluation are encouraged, particularly when the computational component represents the core and driving element of the work. 

The goal of this Special Issue is to provide an up-to-date overview of how computational approaches are advancing modern medicinal chemistry, either independently or in synergy with experimental studies, and to foster interdisciplinary research that accelerates the discovery of effective and safe therapeutic agents. 

We look forward to receiving your contributions.

Dr. Vassilios Myrianthopoulos
Dr. Alessia Bono
Dr. Annamaria Martorana
Prof. 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-anonymized 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

  • rational drug design
  • multi-target drug discovery
  • drug repurposing
  • scaffold design
  • computer-aided drug design (CADD)
  • molecular modeling
  • predictive modeling

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Published Papers (2 papers)

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Research

34 pages, 9825 KB  
Article
MBind: A Web-Based Platform for Metalloprotein and Nonmetalloprotein Docking with Optional ML Docking Integration
by Harrish Ganesh, Sahith Mada, Suhani Aryal, Ronan Zwa, Karen Lainez Diaz, Ishaan Patel and Sivanesan Dakshanamurthy
Molecules 2026, 31(15), 2703; https://doi.org/10.3390/molecules31152703 - 3 Aug 2026
Viewed by 668
Abstract
Metalloprotein docking is difficult because it requires precise preparation of the grid box, charge assignment, and metal coordination geometry. Many current graphical user interfaces (GUIs) do not support these steps well. Here, we present MBind v2026, a web-based GUI that combines AutoDock Vina, [...] Read more.
Metalloprotein docking is difficult because it requires precise preparation of the grid box, charge assignment, and metal coordination geometry. Many current graphical user interfaces (GUIs) do not support these steps well. Here, we present MBind v2026, a web-based GUI that combines AutoDock Vina, AutoGrid4 and AutoDock4 into a single workflow for standard docking and metalloprotein docking. The interface also has the optional machine learning (ML) docking program GNINA v1.0. The main validated metalloprotein workflow in MBind is based on AutoDock4Zn parameterization for zinc, while preliminary workflows for magnesium, iron, and copper are also included. MBind was benchmarked on eight zinc metalloproteins and eight nonmetalloprotein targets. Its performance was compared with ML pose prediction methods (GNINA v1.0, EquiBind v2026, TankBind v2026, and GAABind v2026), cofolding models (Boltz-2 v2026, and AlphaFold 3 v2026), ML affinity prediction tools (StructureNet v2026, GNNSeq v2026, and PLAIG v2026), and non-ML docking tools (SwissDock v2026, CB-Dock2 v2026, Webina v2026, 1-ClickDock v2026, and MolModa v1.01). Pose accuracy was measured by symmetry-aware ligand RMSD using DockRMSD v1.1 under redocking conditions with co-crystal-defined binding sites. Binding energy trends were evaluated by comparing docking scores with IC50-derived ΔG values using mean absolute error. On the zinc metalloprotein benchmark set, MBind produced a mean RMSD of 0.49 Å. On the nonmetalloprotein set, the mean RMSD was 0.62 Å. These results show that MBind can execute zinc metalloprotein and nonmetalloprotein docking workflows through a web-based interface, while the preliminary Mg, Fe, and Cu workflows require additional validation. The MBind GUI web-based platform is publicly available. Full article
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32 pages, 26755 KB  
Article
Novel Sulfonate Derivatives Functionalized with Triazole–Hydrazone Moieties: Synthesis, Characterization, DFT, Targeting Brain Tumors via DNA Damage, Cytotoxicity, Migration Suppression, Antimicrobial Activity, and In Silico Study
by Yasemin Ünver, Meryem Evecen, Fatih Çelik, Ali Aydın, Halil İbrahim Güler, Kadriye İnan Bektaş and Tuğba Usta
Molecules 2026, 31(13), 2281; https://doi.org/10.3390/molecules31132281 - 30 Jun 2026
Viewed by 602
Abstract
In this study, a new series of (E)-4-((2-(2-(4-amino-3-methyl-5-oxo-4,5-dihydro-1H-1,2,4-triazol-1-yl)acetyl)hydrazono)methyl)phenyl 4-halogenobenzenesulfonates (3a3d), where 3a = F, 3b = Cl, 3c = Br, and 3d = I, were successfully synthesized via a straightforward synthetic route. The structures of the obtained compounds were [...] Read more.
In this study, a new series of (E)-4-((2-(2-(4-amino-3-methyl-5-oxo-4,5-dihydro-1H-1,2,4-triazol-1-yl)acetyl)hydrazono)methyl)phenyl 4-halogenobenzenesulfonates (3a3d), where 3a = F, 3b = Cl, 3c = Br, and 3d = I, were successfully synthesized via a straightforward synthetic route. The structures of the obtained compounds were fully characterized and confirmed by spectroscopic techniques, including FT-IR, 1H NMR, and 13C NMR, as well as LC-MS/MS analysis. 1,2,4-triazole-based hydrazone derivatives (3a3d) were investigated using IR and NMR spectroscopy and DFT calculations. Intermolecular interactions, HOMO-LUMO, dipole moment, polarization, first-order hyperpolarizability, and molecular electrostatic potential studies on the molecules were examined. The HOMO and LUMO energy gap study supports the charge transfer probability in the molecules. These were conducted to investigate the reactivity and stability of heterocyclic molecules in bioactivity analysis. Electron density mapping within the molecular electrostatic potential plot and electrostatic potential representation within the iso-surface plot evaluated the concept of charge distribution in the molecule as nucleophilic reactions and electrophilic regions. The predicted nonlinear optical (NLO) properties of the molecules are much greater than those of urea. The results obtained from these investigations collectively provide evidence that the molecules possess nonlinear optical applications. Novel triazole–hydrazone-functionalized aryl sulfonate derivatives (3a3d) were evaluated for their anticancer potential against a panel of brain and non-brain cancer cell lines. Compound 3b exhibited the most favorable overall biological profile, displaying potent activity against SH-SY5Y neuroblastoma (GI = 7.59 μM) and U87MG glioblastoma cells (GI = 13.85 μM), together with the lowest toxicity toward normal FL fibroblasts (GI = 62.02 μM). Compounds 3c and 3d demonstrated remarkable potency against IDHmut-U87 glioma cells (GI = 3.87 and 3.27 μM, respectively), although their selectivity toward cancer cells was limited. DNA degradation studies revealed substantial fragmentation, particularly in C6 and SH-SY5Y cells, while migration assays indicated reduced cellular motility. Molecular docking studies identified compound 3b as the strongest PI3Kα binder, supporting a possible. In addition, the antimicrobial activities of compounds 3a3d were evaluated against selected Gram-positive and Gram-negative bacteria as well as Candida species using the broth microdilution method. The compounds exhibited measurable antimicrobial effects with MIC values ranging from 156 to 625 µg/mL, showing moderate growth inhibition against the tested microorganisms. Although the observed activity was lower than that of the reference antimicrobial agents, the results indicate that these triazole–hydrazone derivatives possess a detectable level of antimicrobial activity and provide a basis for further structural optimization. Collectively, the results suggest that compound 3b represents the most promising lead structure due to its balanced combination of potency, selectivity, and predicted target engagement. Molecular docking was performed to evaluate the binding potential of newly synthesized triazole derivatives (3a3d) against PI3Kα. The docking protocol was validated by re-docking alpelisib, yielding an RMSD of 0.64 Å. Among the tested compounds, 3b showed the most favorable binding energy (−9.94 kcal/mol) and estimated Ki value (52.13 nM), consistent with its superior in vitro activity. Its interactions with key PI3Kα residues, including Val851, Ser854, Met922, and Asp933, support a stable binding mode within the ATP-binding pocket. In silico ADME and toxicity analyses suggested acceptable drug-likeness characteristics, absence of major hepatotoxic, mutagenic, and carcinogenic liabilities, and moderate predicted acute toxicity profiles. These findings suggest that 3b is the most promising derivative for further validation. Full article
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