Identify drug-drug interactions via deep learning: A real world study.
10.1016/j.jpha.2025.101194
- Author:
Jingyang LI
1
;
Yanpeng ZHAO
2
;
Zhenting WANG
3
;
Chunyue LEI
4
;
Lianlian WU
5
;
Yixin ZHANG
2
;
Song HE
2
;
Xiaochen BO
2
;
Jian XIAO
1
Author Information
1. Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China.
2. Academy of Military Medical Sciences, Beijing, 100850, China.
3. Department of Pharmacy, People's Hospital of Qingshen, Meishan, Sichuan, 620460, China.
4. North China University of Technology, No. 5 Jinyuonzhuang Rood, Shijingshan District, Beijing, 100144, China.
5. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
- Publication Type:Journal Article
- Keywords:
Deep learning;
Drug-drug interactions;
Health care;
Multi-dimensional feature fusion
- From:
Journal of Pharmaceutical Analysis
2025;15(6):101194-101194
- CountryChina
- Language:English
-
Abstract:
Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.
