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Practical Applications of Chemoinformatics in Medicinal Chemistry and Drug Discovery

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2414

Special Issue Editors


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Guest Editor
Department of Life Science Informatics, B‐IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich‐Wilhelms‐Universität, Bonn, Germany
Interests: chemoinformatics; medicinal chemistry; chemical biology; drug design; drug discovery
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden 2. Department of Life Science Informatics, B‐IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich‐Wilhelms‐Universität, Bonn, Germany
Interests: chemoinformatics; medicinal chemistry; chemical biology; drug design; drug discovery

Special Issue Information

Dear Colleagues,

The concepts of big data and deep learning are receiving increasing attention in many scientific areas including medicinal chemistry and drug discovery. As a result, the number of computational studies including deep machine learning is growing rapidly. However, such computational studies are often difficult to reconcile or reproduce. Moreover, practical medicinal chemistry or other drug discovery applications of artificial intelligence approaches are still rare, which makes it difficult to assess their actual impact on discovery programs.

This Special Issue aims to highlight studies bridging between chemoinformatics and practical medicinal chemistry or reporting computational approaches for interdisciplinary drug discovery research. Original research and review articles are encouraged that focus on drug discovery-relevant topics including (but not limited to):

  • Big data in medicinal chemistry and drug discovery
  • Hit identification, hit-to-lead, and lead optimization
  • Molecular property prediction (e.g., bioactivity, ADME, and safety)
  • Generative compound design
  • Computer-assisted synthesis prediction

Prof. Dr. Jürgen Bajorath
Dr. Filip Miljković
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.

Prof. Dr. Jürgen Bajorath
Dr. Filip Miljković
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

  • chemoinformatics
  • medicinal chemistry
  • drug discovery
  • big data
  • machine learning
  • deep learning
  • molecular property prediction
  • generative design
  • synthesis prediction

Published Papers (1 paper)

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Research

20 pages, 3151 KiB  
Article
Modeling of Anticancer Sulfonamide Derivatives Lipophilicity by Chemometric and Quantitative Structure-Retention Relationships Approaches
by Monika Pastewska, Beata Żołnowska, Strahinja Kovačević, Hanna Kapica, Maciej Gromelski, Filip Stoliński, Jarosław Sławiński, Wiesław Sawicki and Krzesimir Ciura
Molecules 2022, 27(13), 3965; https://doi.org/10.3390/molecules27133965 - 21 Jun 2022
Cited by 5 | Viewed by 2001
Abstract
Sulfonamides are a classic group of chemotherapeutic drugs with a broad spectrum of pharmacological action, including anticancer activity. In this work, reversed-phase high-performance liquid chromatography and biomimetic chromatography were applied to characterize the lipophilicity of sulfonamide derivatives with proven anticancer activities against human [...] Read more.
Sulfonamides are a classic group of chemotherapeutic drugs with a broad spectrum of pharmacological action, including anticancer activity. In this work, reversed-phase high-performance liquid chromatography and biomimetic chromatography were applied to characterize the lipophilicity of sulfonamide derivatives with proven anticancer activities against human colon cancer. Chromatographically determined lipophilicity parameters were compared with obtained logP, employing various computational approaches. Similarities and dissimilarities between experimental and computational logP were studied using principal component analysis, cluster analysis, and the sum of ranking differences. Furthermore, quantitative structure–retention relationship modeling was applied to understand the influences of sulfonamide’s molecular properties on lipophilicity and affinity to phospholipids. Full article
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