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Synthetic Studies Aimed at Heterocyclic Organic Compounds, 2nd Edition

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 502

Special Issue Editor


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Guest Editor
Schuylkill Campus, Pennsylvania State University, 200 University Drive, Schuylkill Haven, PA 17972, USA
Interests: organic synthesis; heterocycles; drug discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Organic heterocycles are a broad but very important category of molecules. They are widely found in compounds of medicinal and agricultural use. Many of them are considered privileged structures. They are also biologically important, found in groups such as nucleic acids and carbohydrates, as well as many other natural products. They are, thus, important targets of synthetic chemistry. This Special Issue will focus on the synthesis of heterocyclic compounds. Papers are welcomed that describe the synthesis of specific heterocycles, as well as papers that disclose methods or strategies that could be applied to heterocyclic synthesis. Papers may be articles, communications, or reviews.

Dr. Lee J. Silverberg
Guest Editor

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

  • heterocycles
  • synthesis
  • methods
  • medicinal
  • agricultural

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Published Papers (1 paper)

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Research

13 pages, 2289 KiB  
Article
A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials
by Yusuke Kanematsu, Akiyoshi Ohta, Shunya Nagai, Yohei Adachi, Hiromasa Kaneko, Takayoshi Ishimoto, Takio Kurita and Joji Ohshita
Molecules 2025, 30(8), 1686; https://doi.org/10.3390/molecules30081686 - 10 Apr 2025
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Abstract
We have built a prediction model of the fluorescence quantum yields of metalloles. Based on the suggestion by the prediction model, we synthesized 10 fluorescent molecules to confirm the prediction accuracy. By measuring the fluorescence quantum yields of the synthesized molecules, it was [...] Read more.
We have built a prediction model of the fluorescence quantum yields of metalloles. Based on the suggestion by the prediction model, we synthesized 10 fluorescent molecules to confirm the prediction accuracy. By measuring the fluorescence quantum yields of the synthesized molecules, it was demonstrated that our prediction model reasonably classified the quantum yields with an accuracy of 0.7. In particular, the low quantum yields were perfectly predicted for the synthesized molecules, demonstrating the usefulness of our prediction model to screen out weakly fluorescent molecules from the candidates. On the other hand, the low precision of 0.5 was attributed to the bias in the training dataset containing many fluorine-containing molecules with high quantum yields. Our prediction model was then revised with the generator of candidate molecular structures for more efficient development of fluorescent materials with taking the applicability domain into account, and the improvement of the applicability was confirmed owing to the increment of the dataset. Full article
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