Abstract
Identifying Drug-Target interactions is a core component in the development of new therapeutics, the repurposing of existing drugs, and the mechanistic research. To efficiently identify target sites for drugs, we propose the Drug-Target Explorer, a real-time system for extracting drug-target relationships from vast literature. This system employs a multi-stage intelligent retrieval strategy, leveraging dual large language models to simultaneously extract structured target information from original PubMed literature. Each result includes original evidence with links to PubMed source publications. Drug-Target Explorer supports conditional filtering, model comparisons, and one-click export, reducing the technical barriers and time investment required for target discovery.
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References
Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., Sontag, D.: Large language models are few-shot clinical information extractors. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 1998–2022 (2022)
Lai, Y., et al.: Inferring drug-gene relationships in cancer using literature-augmented large language models. Cancer Res. Commun. 5(4), 706–718 (2025)
Lan, W., et al.: The large language models on biomedical data analysis: a survey. IEEE J. Biomed. Health Info. 29(6), 4486–4497 (2025)
Patne, A.Y., Dhulipala, S.M., Lawless, W., Prakash, S., Mohapatra, S.S., Mohapatra, S.: Drug discovery in the age of artificial intelligence: transformative target-based approaches. Int. J. Mol. Sci. 25(22), 12233 (2024)
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© 2026 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Su, L., Song, Y., Qian, W., He, Y., Shao, M., Zhang, L. (2026). Drug-Target Explorer: An Interactive System for Real-Time Extraction of Drug-Target Relationships Based on Large Language Models. In: Jung, H., Wang, T., Toyoda, M., Kwon, HY., Lee, Jw. (eds) Database Systems for Advanced Applications. DASFAA 2026. Lecture Notes in Computer Science, vol 16540. Springer, Singapore. https://doi.org/10.1007/978-981-92-0378-9_54
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DOI: https://doi.org/10.1007/978-981-92-0378-9_54
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