A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature
Yaxun Jia, Zhu Yuan, Lian Zhu, Zuo-lin Xiang
Abstract
Drug–drug interactions (DDIs) are a significant source of adverse drug events and pose critical challenges to patient safety and clinical decision-making. Extracting DDIs from biomedical literature plays an essential role in pharmacovigilance, yet remains difficult due to data sparsity and high annotation costs.
Introduction
Drug–drug interactions (DDIs) are a critical factor in pharmacovigilance and clinical decision-making, as they can significantly alter the efficacy or safety of co-administered drugs. In clinical settings, especially among elderly patients or those undergoing polypharmacy, unrecognized DDIs can lead to adverse drug reactions (ADRs), increased hospitalization, and even mortality [1–3].
Materials and method
This section presents the architecture and training strategy of BioMCL-DDI, a unified meta-contrastive learning framework for few-shot drug–drug interaction extraction. The proposed model is designed to address two major challenges in clinical NLP systems: data sparsity, where annotated biomedical DDI examples are limited, and class imbalance, where frequent and rare interaction types co-exist in skewed distributions.
Results
In this section, we present a comprehensive evaluation of the proposed BioMCL-DDI framework. We aim to demonstrate its effectiveness in few-shot drug–drug interaction (DDI) extraction through extensive comparisons with full-supervised and few-shot baselines, as well as detailed analyses including ablation studies, learning behavior, transferability, and error inspection.
Discussion
This study presents BioMCL-DDI, a unified few-shot learning framework for drug–drug interaction (DDI) extraction that integrates prototypical classification and contrastive representation learning. Our empirical results demonstrate that the proposed model achieves strong performance under low-resource conditions, outperforming existing fully supervised and meta-learning baselines on both in-domain and cross-domain settings.
Conclusion
This paper introduced BioMCL-DDI, a unified meta-contrastive learning framework for few-shot drug–drug interaction (DDI) extraction. It performs a fine-grained, multi-class classification of drug pairs into five distinct DDI types: DDI-false, DDI-effect, DDI-mechanism, DDI-advise, and DDI-int. By jointly optimizing prototypical classification and instance-level contrastive learning within a fully supervised setting, BioMCL-DDI achieves robust performance under data-scarce conditions without relying on episodic task construction or pretraining.
Citation: Jia Y, Yuan Z, Zhu L, Xiang Z-l (2025) A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature. PLoS Comput Biol 21(12): e1013722. https://doi.org/10.1371/journal.pcbi.1013722
Editor: Qiangguo Jin, Northwestern Polytechnical University, CHINA
Received: August 5, 2025; Accepted: November 7, 2025; Published: December 5, 2025
Copyright: © 2025 Jia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All our code and data have been publicly released at: https://github.com/Hero-Legend/BioMCL-DDI.
Funding: This work is supported by National Natural Science Foundation of China (82160591 to Zl.X.), Key Project of Clinical Research of Shanghai East Hospital, Tongji University (DFLC2022012 to Zl.X.), Key Specialty Construction Project of Shanghai Pudong New Area Health Commission (PWZzk2022-02 to Zl.X.), Funded by Outstanding Leaders Training Program of Pudong Health Bureau of Shanghai (PWR12023-02 to Zl.X.) and Shanghai Science Technology Innovation Action Plan (23Y11909000 to Zl.X.), Science Research Project of Hebei Education Department (QN2025011 to Z.Y.) and Doctoral Research Start-up Fund Program of The National Police University for Criminal Justice (BSQDW202150 to Z.Y.). Zuo-lin Xiang is the corresponding author of this paper. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
