Improving drug-drug interaction prediction via in-context learning and judging with large language models

These findings highlight the potential of LLMs in advancing drug discovery through more effective DDI prediction. The modular prompt structure, combined with ensemble reasoning, offers a scalable framework for knowledge-intensive biomedical applications. The code for DDI-JUDGE is available at https://github.com/zcc1203/ddi-judge.

Authors

Tianyi Zhao (ORCID: https://orcid.org/0000-0003-1913-081X)
Xiaoqiang Li (ORCID: https://orcid.org/0000-0002-2782-2632)
Chengcheng Zhang (ORCID: https://orcid.org/0000-0003-0505-7607)
He Qi

Institutions

Chinese Academy of Sciences (CN)
Harbin Institute of Technology (CN)
National Medical Products Administration (CN)
Suzhou Institute of Biomedical Engineering and Technology (CN)

Publication Details

Journal: Frontiers in Pharmacology
Published: 2025-06-02
DOI: https://doi.org/10.3389/fphar.2025.1589788
Citations: 6
Primary Topic: Computational Drug Discovery Methods
Type: article
Field-Weighted Citation Impact: 9.03

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article

Improving drug-drug interaction prediction via in-context learning and judging with large language models

Tianyi Zhao, Xiaoqiang Li, Chengcheng Zhang, He Qi

Jun 2, 2025

6 citations

Frontiers in Pharmacology

Computational Drug Discovery Methods

9.03

article

Improving drug-drug interaction prediction via in-context learning and judging with large language models

Tianyi Zhao, Xiaoqiang Li, Chengcheng Zhang, He Qi

June 2, 2025article en

6 citations

Abstract

These findings highlight the potential of LLMs in advancing drug discovery through more effective DDI prediction. The modular prompt structure, combined with ensemble reasoning, offers a scalable framework for knowledge-intensive biomedical applications. The code for DDI-JUDGE is available at https://github.com/zcc1203/ddi-judge.

Frontiers in PharmacologyVol. 16

Chinese Academy of Sciences (CN), Harbin Institute of Technology (CN), National Medical Products Administration (CN), Suzhou Institute of Biomedical Engineering and Technology (CN)

Openalex Percentile: Top 2%

Computational Drug Discovery Methods

9.03

DOI Open Access PDF OpenAlex

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Improving drug-drug interaction prediction via in-context learning and judging with large language models — Tianyi Zhao, Xiaoqiang Li, et al. · Frontiers in Pharmacology (2025) | TGRS Research Map | TGRS