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Syntheses and Applications in Medicinal Chemistry

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 1380

Special Issue Editors


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Guest Editor
Department of Pathology and Immunology, Baylor College of Medicine, 1 Baylor Plz, Houston, TX 77030, USA
Interests: total synthesis; medicinal chemistry

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Guest Editor
U.R.I.To.N. Research Unit, UNIFI · Dipartimento NEUROFARBA, University of Florence, Florence, Italy
Interests: medicinal chemistry; drugs; toxicology; enzymes; X-ray crystallography
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Guest Editor
Department of Organic Chemistry, Faculty of Chemistry, University of Plovdiv, 4000 Plovdiv, Bulgaria
Interests: synthesis; drug discovery; medicinal chemistry; heterocyclic compounds; organic synthesis; hybrid molecules; nanoparticles; drug-delivery systems; drug-design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the publication of reliable and reproducible research papers that unite chemistry and biology in order to study, understand, and cure biological disorders and diseases. Due to recent advancements in synthesis methodologies, numerous novel reactions and reagents have been discovered and developed, thereby expanding the toolbox of medicinal chemistry. These new developments are invaluable assets to the field, enabling researchers to prepare compounds with novel three-dimensional chemical structures to probe target biomolecules, and enhance the activity, selectivity, and metabolic stability of compounds. Another crucial aspect of the development of novel methods in chemistry is reliability and reproducibility. During late-stage drug development, reliable and reproducible reactions are essential for generating drug materials with a consistent quantity and quality for clinical trials. Any chemistry studies related to research from early-stage discovery to late-stage clinical studies will be included in this Special Issue.

Reflecting the traditional core subjects of medicinal chemistry, organic chemistry, bio-organic chemistry, and analytical chemistry, this Special Issue also features a broad range of biochemical research; this includes, but is not limited to, computational and theoretical chemistry, green chemistry, polymer chemistry, and nanotechnology. Other cross-disciplinary topics such as bioinorganic chemistry, organometallic chemistry, enzyme engineering, bioinformatics, and biotransformation will also be featured. Manuscripts detailing multidisciplinary research at the interface of drug discovery and other scientific fields such as natural product synthesis, biology, pharmacology, and materials science are also encouraged; this is provided that the central theme of the article and the advances it presents contribute to the development of a drug or medical device for clinical use.

Dr. Qiuji Ye
Dr. Fabrizio Carta
Dr. Stoyanka Atanasova
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. Applied Sciences 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 2400 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

  • medicinal chemistry
  • drug discovery
  • drug metabolism
  • synthesis
  • organic chemistry
  • chemical biology
  • disorders and diseases

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Published Papers (1 paper)

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Research

12 pages, 685 KiB  
Article
Multi-Label Classification for Predicting Antimicrobial Resistance on E. coli
by Prince Delator Gidiglo, Soualihou Ngnamsie Njimbouom, Gelany Aly Abdelkader, Soophia Mosalla and Jeong-Dong Kim
Appl. Sci. 2024, 14(18), 8225; https://doi.org/10.3390/app14188225 - 12 Sep 2024
Viewed by 1072
Abstract
Antimicrobial resistance (AMR) represents a pressing global health challenge with implications for developmental progress, as it increasingly manifests within pathogenic bacterial populations. This phenomenon leads to a substantial public health hazard, given its capacity to undermine the efficacy of medical interventions, thereby jeopardizing [...] Read more.
Antimicrobial resistance (AMR) represents a pressing global health challenge with implications for developmental progress, as it increasingly manifests within pathogenic bacterial populations. This phenomenon leads to a substantial public health hazard, given its capacity to undermine the efficacy of medical interventions, thereby jeopardizing patient welfare. In recent years, an increasing number of machine learning methods have been employed to predict antimicrobial resistance. However, these methods still pose challenges in single-drug resistance prediction. This study proposed an effective model for predicting antimicrobial resistance to E. Coli by utilizing the eXtreme Gradient Boosting model (XGBoost), among ten other machine learning methods. The experimental results demonstrate that XGBoost outperforms other machine learning classification methods, particularly in terms of precision and hamming loss, with scores of 0.891 and 0.110, respectively. Our study explores the existing machine learning models for predicting antimicrobial resistance (AMR), thereby improving the diagnosis as well as treatment of infections in clinical settings. Full article
(This article belongs to the Special Issue Syntheses and Applications in Medicinal Chemistry)
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