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Trends and Prospects in Computer-Aided Drug Design

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1894

Special Issue Editors


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Guest Editor
Kragujevac Center for Computational Biochemistry, Department of Chemistry, Faculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, P.O. Box 60, Serbia
Interests: biochemistry; computer-aided drug design; cheminformatics; bioinformatics
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Special Issue Information

Dear Colleagues,

Computer-aided drug design (CADD) comprises ligand-based (LB) and structure-based (SB) in silico methods, either conventional or modern (viz. artificial intelligence), being routinely applied in drug discovery in a continuously growing fashion. Upon reaching a good level of accuracy and predictability, CADD methods are nowadays trusted in prioritizing new and untested molecular entities to be acquired or synthesized, thus overcoming the intrinsic limitations of traditional synthetic protocols (low efficiency and high cost), allowing the reduction in unnecessary waste of in vitro and in vivo resources.

Therefore, either LB or SB CADD approaches are valuable tools to study how two different molecular entities can recognize each other, and to foresee their biological effect. Although many LB or SB CADD tools have been disclosed, new LB or SB CADD programs and applications are constantly being announced. Due to the limitations of current technology (either software or hardware), we are still far from reaching the full potential of LB or SB CADD application in drug design; the next-generation accuracy and predictability still have to be reached by means of improving the conventional LB or SB CADD approaches with the aid of artificial intelligence. Therefore, there is still urgency to continue the improvement of actual LB or SB procedures, and this represents the main goal of the herein announced Special Issue. Manuscripts are welcome to be submitted if reporting new approaches in LB or SB CADD or the application of the latest technology to either prioritize synthetically accessible molecules or optimize plant mixtures for further pharmacological evaluation.

The Special Issue “Trends and Prospects in Computer-Aided Drug Design” will embrace several LB or SB methods in CADD, such as:

  • Ligand-based drug design: current methods vs. artificial intelligence;
  • Ligand-based QSAR and QSPR modeling in drug design;
  • Proteochemometrics modeling in drug design;
  • Development of methods for quality assessments of topological descriptors;
  • Artificial intelligence in the development of molecular descriptors;
  • Ligand-based alignment software development;
  • Ligand-based 3D QSAR in drug design;
  • Ligand-based 3D QSAR software development;
  • Ligand-based 3D pharmacophore modeling in drug design;
  • Ligand-based 3D pharmacophore software development;
  • Ligand-based design using medicinal chemistry plants as a source of bioactive compounds conducted by means of artificial intelligence;
  • Analysis of drug mixture effects;
  • Structure-based drug design: current methods vs. artificial intelligence;
  • Predicting drug activity/properties;
  • Structure-based 3D QSAR in drug design;
  • 3D QSAR software development;
  • Structure-based 3D pharmacophore modeling in drug design;
  • Structure-based analysis of bioactive conformations;
  • Small-molecule reversible docking application;
  • Small-molecule covalent docking application;
  • Protein–protein docking;
  • Molecular docking software development;
  • Molecular docking software comparison;
  • Scoring function development;
  • Scoring function comparison;
  • SB/LB consensus scoring strategies in drug design;
  • Virtual screening;
  • Host–guest studies;
  • Molecular dynamics as molecular docking tools;
  • Design of selective drugs;
  • Polypharmacology drug design;
  • Drug toxicity prediction;
  • De novo drug design using artificial intelligence;
  • Drug bioavailability;
  • Design of drug mixture or combinations;
  • Automated workflows in drug design;
  • Teaching methods in drug design;
  • Reviews in CADD protocols.

Dr. Milan Mladenović
Dr. Rino Ragno
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. 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

  • ligand-based (LB) drug design
  • structure-based (SB) drug design
  • QSAR
  • 3D QSAR
  • 3D pharmacophores
  • artificial intelligence in drug design
  • software development for drug design
  • teaching methods in drug design

Published Papers (1 paper)

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Research

32 pages, 16102 KiB  
Article
Molecular Docking Assessment of Cathinones as 5-HT2AR Ligands: Developing of Predictive Structure-Based Bioactive Conformations and Three-Dimensional Structure-Activity Relationships Models for Future Recognition of Abuse Drugs
by Nevena Tomašević, Maja Vujović, Emilija Kostić, Venkatesan Ragavendran, Biljana Arsić, Sanja Lj. Matić, Mijat Božović, Rossella Fioravanti, Eleonora Proia, Rino Ragno and Milan Mladenović
Molecules 2023, 28(17), 6236; https://doi.org/10.3390/molecules28176236 - 24 Aug 2023
Cited by 1 | Viewed by 1199
Abstract
Commercially available cathinones are drugs of long-term abuse drugs whose pharmacology is fairly well understood. While their psychedelic effects are associated with 5-HT2AR, the enclosed study summarizes efforts to shed light on the pharmacodynamic profiles, not yet known at the receptor [...] Read more.
Commercially available cathinones are drugs of long-term abuse drugs whose pharmacology is fairly well understood. While their psychedelic effects are associated with 5-HT2AR, the enclosed study summarizes efforts to shed light on the pharmacodynamic profiles, not yet known at the receptor level, using molecular docking and three-dimensional quantitative structure–activity relationship (3-D QSAR) studies. The bioactive conformations of cathinones were modeled by AutoDock Vina and were used to build structure-based (SB) 3-D QSAR models using the Open3DQSAR engine. Graphical inspection of the results led to the depiction of a 3-D structure analysis-activity relationship (SAR) scheme that could be used as a guideline for molecular determinants by which any untested cathinone molecule can be predicted as a potential 5-HT2AR binder prior to experimental evaluation. The obtained models, which showed a good agreement with the chemical properties of co-crystallized 5-HT2AR ligands, proved to be valuable for future virtual screening campaigns to recognize unused cathinones and similar compounds, such as 5-HT2AR ligands, minimizing both time and financial resources for the characterization of their psychedelic effects. Full article
(This article belongs to the Special Issue Trends and Prospects in Computer-Aided Drug Design)
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