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Molecules
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Fragment-Based Drug Discovery II
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Fragment-Based Drug Discovery II

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 2372

Special Issue Editor

Prof. Dr. Brian J. Stockman

E-Mail Website
Guest Editor
Department of Chemistry, Adelphi University, Garden City, NY, USA
Interests: fragment-based drug discovery; 1H and 19F NMR-based activity assays; nucleoside ribohydrolase inhibitors as novel antitrichomonal agents
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to share with you the second edition of “Fragment Based Drug Discovery II”.

Fragment-based drug discovery has become widespread in industry and academia. The utility of the approach has been validated by the steadily increasing number of compounds in the clinic that were developed from hits having their origins in fragment screens. The approach continues to be improved by refinements in fragment library design, development of new and often complementary screening methods, and more efficient transitions from fragment hits to structure-guided medicinal chemistry. This Special Issue will disseminate advances in fragment library design and screening methodologies, along with recent applications of fragment-based ligandability assessment and drug discovery to novel pharmaceutical targets.

Prof. Dr. Brian J. Stockman
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

  • Fragment library design
  • Fragment screening
  • Hit-to-lead transition
  • Molecular modeling
  • NMR spectroscopy
  • X-ray crystallography
  • Surface plasmon resonance
  • Target ligandability
  • Protein-protein interaction inhibitors
  • Enzyme inhibitors

Published Papers (1 paper)

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Research

14 pages, 6902 KiB  
Article
Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models
by Mengzhou Bi, Zhen Guan, Tengjiao Fan, Na Zhang, Jianhua Wang, Guohui Sun, Lijiao Zhao and Rugang Zhong
Molecules 2022, 27(6), 1753; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27061753 - 08 Mar 2022
Viewed by 1804
Abstract
Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of [...] Read more.
Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors. Full article
(This article belongs to the Special Issue Fragment-Based Drug Discovery II)
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