Author: Kim, Sijin ; Lee, Namgil ; Jeong, Birang ; Han, Sang-Jun ; Kang, Jeongyeon ; Yoo, Hojin ; Kwak, Hoguen ; Kim, Byung-Seok ; Hong, Donggyun ; Jung, Go-Yeon ; Yang, Heejung

AbstractSignal transducer and activator of transcription 3 (STAT3) is a critical regulator of immune responses, cellular proliferation, and survival, positioning it as a promising therapeutic target in autoimmune diseases and cancer. In this study, we present a chemoproteomics-based approach for the discovery and optimization of small-molecule inhibitors targeting STAT3. A series of novel small molecules capable of selectively modulating STAT3 activity were identified. By integrating structure-based design with our chemoproteomics platform, we not only validated the interactions of these compounds with STAT3 but also elucidated their downstream signaling effects in disease-relevant autoimmune models. Our chemoproteomics platform facilitates the unbiased identification of drug targets and off-target effects at a proteome-wide scale, significantly accelerating the lead optimization process. These findings highlight the potential of our STAT3 inhibitors as a novel class of therapeutics for autoimmune diseases. Ongoing studies are focused on optimizing these compounds for clinical trials by using the chemoproteomics platform to identify their molecular mechanisms of action and to predict potential off-target effects.Citation Format:Heejung Yang, Hojin Yoo, Sang-Jun Han, Sijin Kim, Donggyun Hong, Jeongyeon Kang, Birang Jeong, Go-Yeon Jung, Namgil Lee, Hoguen Kwak, Byung-Seok Kim. Development of hit compounds targeting STAT3 for autoimmune diseases and cancers using a chemoproteomics-driven approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1882.

Scientific ReportsQ3 · CROSS-FIELD

Hierarchical network analysis of co-occurring bioentities in literature

Q3 · CROSS-FIELD

ArticleOA

Author: Park, Beomjun ; Lee, Jiho ; Lee, Namgil ; Park, Jinyoung ; Yang, Heejung ; Yoo, Hojin ; Jang, Hyeon Seok

AbstractBiomedical databases grow by more than a thousand new publications every day. The large volume of biomedical literature that is being published at an unprecedented rate hinders the discovery of relevant knowledge from keywords of interest to gather new insights and form hypotheses. A text-mining tool, PubTator, helps to automatically annotate bioentities, such as species, chemicals, genes, and diseases, from PubMed abstracts and full-text articles. However, the manual re-organization and analysis of bioentities is a non-trivial and highly time-consuming task. ChexMix was designed to extract the unique identifiers of bioentities from query results. Herein, ChexMix was used to construct a taxonomic tree with allied species among Korean native plants and to extract the medical subject headings unique identifier of the bioentities, which co-occurred with the keywords in the same literature. ChexMix discovered the allied species related to a keyword of interest and experimentally proved its usefulness for multi-species analysis.

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