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The nuclear receptor binding SET domain (NSD) family of histone methyltransferases, which comprised NSD1, NSD2, and NSD3. They play a pivotal role in catalyzing mono- and dimethylation of histone H3 at lysine 36 (H3K36me1/2), a modification critical for maintaining chromatin structure and transcriptional fidelity. Dysregulation of NSD enzymes, often through overexpression, mutation, or chromosomal translocation, has been implicated in a broad spectrum of malignancies and various diseases. Due to their critical role in disease pathogenesis and recent technological advances, NSD proteins have become attractive targets for therapeutic intervention. This review highlights recent progress in developing small molecule inhibitors and chemical probes targeting NSD family members, focusing on the catalytic SET domain, the PWWP domain, and other functional motifs. Among these, several chemical classes have been investigated, including quinoline-5,8-dione, 2-aminobenzothiazole, 5-aminonaphthalene, quinazoline, purine, benzoxazinone-cyclopropylamide, and imidazole derivatives. In addition, novel strategies such as protein degradation via PROTACs and dual-target inhibitors are discussed. By systematically summarizing recent advances, this review seeks to facilitate and accelerate the development of effective NSD modulators, ultimately advancing therapeutic options for diseases driven by NSD dysregulation.
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http://dx.doi.org/10.1080/17568919.2025.2557180 | DOI Listing |
J Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFOrg Lett
September 2025
Istanbul Technical University, Chemistry Department, Maslak, Istanbul 34469, Turkey.
A donor-acceptor-donor type π-conjugated small molecule, , having an oligoether-functionalized azaisoindigo unit as an acceptor and triphenylamine units as donor groups was designed and synthesized. Its opto-electrochemical properties and charge transport applications were investigated. demonstrated p-type transport behavior with a maximum carrier mobility of 0.
View Article and Find Full Text PDFJ Am Chem Soc
September 2025
Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
Narrow electrochemical windows and high reactivity of aqueous solutions remain critical bottlenecks for the practical application of aqueous batteries. However, the mechanisms for tuning microscopic reactivity of HO molecules in aqueous electrolytes remain elusive. This study employs six ether molecules with distinct structures and solvation powers to regulate the microstructure of aqueous solutions.
View Article and Find Full Text PDFBioinformatics
September 2025
Biocomputation and Complex Systems Physics Institute (BIFI)-Joint Unit GBsC-CSIC, University of Zaragoza, Zaragoza, 50018, Spain.
Motivation: The stability of protein interfaces influences protein dynamics and unfolding cooperativity. Although in some cases the dynamics of proteins can be deduced from their topology, much of the stability of an interface is related to the complementarity of the interacting parts. It is also important to note that proteins that display non-cooperative unfolding cannot be rationally stabilized unless the regions that unfold first are known.
View Article and Find Full Text PDFJ Med Chem
September 2025
Encoded Technologies, Molecular Modalities Discovery, GSK, Cambridge, Massachusetts 02140, United States.
DNA-encoded libraries (DELs) are used throughout small-molecule drug discovery to identify new lead compounds for protein targets. DEL hits are traditionally evaluated via off-DNA resynthesis and subsequent biological testing. This approach can be time- and resource-intensive, limiting the number of putative hits selected for follow-up.
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