Building a credibility-based framework for target discovery: Perspectives from tRNA synthetase-linked metabolic diseases.

Jaeyoung Choi, Ina Yoon, YounSung Jung, SeanKyo Han, EunHee Kang, TaeJin Ahn, Sunghoon Kim

Journal: Briefings in bioinformatics 2026;27(2):

PMID: 42047599

Abstract

While target identification is essential for successful drug discovery, no systematic workflow exists to prioritize potential targets for a given indication. Therefore, this study aims to develop an information-based approach combining text mining, network analysis, and centrality-based prioritization. As a case study, we applied this workflow to identify metabolic disease targets potentially linked to aminoacyl-tRNA synthetases (ARSs). From 1,407,654 PubMed articles, potential ARS interactors and their disease associations were mined. Using these data, the ARS interactor-disease networks were constructed based on edge frequency and citation count. To assess the reliability of these linkages, we used five centrality indices with novel visualization tools and identified 94 high-credibility disease-associated ARS interactors. Among them, two targets (ESR1 and APP) were selected for experimental validation. Although demonstrated in ARS-mediated metabolic diseases, this approach can be similarly used to identify disease-associated factors with credibility scores within any target space of interest.

Address: Institute for Artificial Intelligence and Biomedical Research, Medicinal Bioconvergence Research Center, College of Pharmacy, Yonsei University, Incheon 21983, Republic of Korea.; Department of Pharmacy and Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon 21983, Republic of Korea.; Department of Pharmacy and Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon 21983, Republic of Korea.; Department of Integrative Biotechnology, Yonsei University, Incheon 21983, Republic of Korea.; Department of Advanced Convergence, Graduate School, Handong Global University, Pohang 37554, Republic of Korea.; Department of Life Science, Handong Global University, Pohang 37554, Republic of Korea.; Institute for Artificial Intelligence and Biomedical Research, Medicinal Bioconvergence Research Center, College of Pharmacy, Yonsei University, Incheon 21983, Republic of Korea.; Department of Integrative Biotechnology, Yonsei University, Incheon 21983, Republic of Korea.; Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Incheon 21983, Republic of Korea.; College of Medicine, Gangnam Severance Hospital, Yonsei University, Seoul 06273, Republic of Korea.

DOI: 10.1093/bib/bbag195

MeSH Terms: Humans, Amino Acyl-tRNA Synthetases, Metabolic Diseases, Drug Discovery, Data Mining