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Computational Studies and Drug Design

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 682

Special Issue Editor

Special Issue Information

Dear Colleagues,

Computational methods include essential strategies for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug design and drug repurposing. These approaches are supported by ligand- and structure-based calculations. The identification of novel putative drugs requires experimental assays to confirm the therapeutic effectiveness and the safety profile of the proposed derivatives. In this context, the design of multi-target compounds as well as repurposing campaigns open intriguing opportunities towards novel drug-like molecules. While the discovery of a multi-target scaffold allows the development of new drugs with different biological effects, drug repurposing, or repositioning, is a strategy employed in pursuit of novel therapeutic applications for already-approved drugs, expanding their usage spectrum beyond primary medical indications.

This Special Issue will cover all different aspects of mechanism-based computational methods in drug design, as confirmed by following experimental data for the newly developed compounds. It invites both reviews and original articles regarding the design and optimization of multi-target-compounds or repositioned ligands, by molecular modelling techniques and ADMET property prediction. Structure-based studies deciphering the mechanism of action of newly synthesized derivatives are also desired. This Special Issue aims to summarize the state of the art and the latest studies in medicinal chemistry thanks to mechanism-based computer-assisted drug design.

Dr. Elena Cichero
Guest Editor

Manuscript Submission Information

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Keywords

  • virtual screening
  • repositioning
  • multi-target compounds
  • molecular docking
  • pharmacophore modeling
  • ADMET

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Published Papers (1 paper)

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Research

24 pages, 8400 KB  
Article
Unveiling the Structure of PROT and ATB0,+: Unique Members of the Glycine Transporter Subfamily
by Dorota Stary and Marek Bajda
Molecules 2025, 30(22), 4412; https://doi.org/10.3390/molecules30224412 - 14 Nov 2025
Viewed by 411
Abstract
The proline transporter (PROT, SLC6A7) and the neutral and cationic amino acid transporter (ATB0,+, SLC6A14) belong to the glycine transporter subfamily, exhibiting distinct substrate specificities and physiological functions. PROT modulates neurotransmission through proline transport in the brain, while ATB0,+ facilitates [...] Read more.
The proline transporter (PROT, SLC6A7) and the neutral and cationic amino acid transporter (ATB0,+, SLC6A14) belong to the glycine transporter subfamily, exhibiting distinct substrate specificities and physiological functions. PROT modulates neurotransmission through proline transport in the brain, while ATB0,+ facilitates nutrient uptake, especially in the gastrointestinal tract. Impaired function of PROT has been associated with neurological disorders, while ATB0,+ overexpression has been linked to cancers. Despite their biological relevance, the pool of known ligands for these transporters is limited, and their exact 3D structures remain unknown. Therefore, we conducted an in silico analysis of PROT and ATB0,+ and compared the obtained results with available literature data on the glycine transporter GlyT1, from the same subfamily. Using homology modelling, docking studies, and molecular dynamics simulations, we investigated the structural properties of PROT and ATB0,+ and described protein–ligand interactions. We pointed crucial residues responsible for ligand binding, including Tyr133, Tyr297, Phe303, and Phe403 in PROT and Trp327, Val128, and Tyr321 in ATB0,+. This work provides new insights into the molecular features of PROT and ATB0,+ transporters, which could support the development of novel transporter inhibitors. Full article
(This article belongs to the Special Issue Computational Studies and Drug Design)
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