Identify drug-drug interactions via deep learning: A real world study
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 Mult...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Journal of Pharmaceutical Analysis |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095177925000115 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849318362896662528 |
|---|---|
| author | Jingyang Li Yanpeng Zhao Zhenting Wang Chunyue Lei Lianlian Wu Yixin Zhang Song He Xiaochen Bo Jian Xiao |
| author_facet | Jingyang Li Yanpeng Zhao Zhenting Wang Chunyue Lei Lianlian Wu Yixin Zhang Song He Xiaochen Bo Jian Xiao |
| author_sort | Jingyang Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e02d272a3e7346688d5085e96d0b482f |
| institution | Kabale University |
| issn | 2095-1779 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pharmaceutical Analysis |
| spelling | doaj-art-e02d272a3e7346688d5085e96d0b482f2025-08-20T03:50:50ZengElsevierJournal of Pharmaceutical Analysis2095-17792025-06-0115610119410.1016/j.jpha.2025.101194Identify drug-drug interactions via deep learning: A real world studyJingyang Li0Yanpeng Zhao1Zhenting Wang2Chunyue Lei3Lianlian Wu4Yixin Zhang5Song He6Xiaochen Bo7Jian Xiao8Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, ChinaAcademy of Military Medical Sciences, Beijing, 100850, ChinaDepartment of Pharmacy, People's Hospital of Qingshen, Meishan, Sichuan, 620460, ChinaNorth China University of Technology, No. 5 Jinyuonzhuang Rood, Shijingshan District, Beijing, 100144, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, ChinaAcademy of Military Medical Sciences, Beijing, 100850, ChinaAcademy of Military Medical Sciences, Beijing, 100850, China; Corresponding author.Academy of Military Medical Sciences, Beijing, 100850, China; Corresponding author.Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China; Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China; Corresponding author. Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, China.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.http://www.sciencedirect.com/science/article/pii/S2095177925000115Drug-drug interactionsDeep learningHealth careMulti-dimensional feature fusion |
| spellingShingle | Jingyang Li Yanpeng Zhao Zhenting Wang Chunyue Lei Lianlian Wu Yixin Zhang Song He Xiaochen Bo Jian Xiao Identify drug-drug interactions via deep learning: A real world study Journal of Pharmaceutical Analysis Drug-drug interactions Deep learning Health care Multi-dimensional feature fusion |
| title | Identify drug-drug interactions via deep learning: A real world study |
| title_full | Identify drug-drug interactions via deep learning: A real world study |
| title_fullStr | Identify drug-drug interactions via deep learning: A real world study |
| title_full_unstemmed | Identify drug-drug interactions via deep learning: A real world study |
| title_short | Identify drug-drug interactions via deep learning: A real world study |
| title_sort | identify drug drug interactions via deep learning a real world study |
| topic | Drug-drug interactions Deep learning Health care Multi-dimensional feature fusion |
| url | http://www.sciencedirect.com/science/article/pii/S2095177925000115 |
| work_keys_str_mv | AT jingyangli identifydrugdruginteractionsviadeeplearningarealworldstudy AT yanpengzhao identifydrugdruginteractionsviadeeplearningarealworldstudy AT zhentingwang identifydrugdruginteractionsviadeeplearningarealworldstudy AT chunyuelei identifydrugdruginteractionsviadeeplearningarealworldstudy AT lianlianwu identifydrugdruginteractionsviadeeplearningarealworldstudy AT yixinzhang identifydrugdruginteractionsviadeeplearningarealworldstudy AT songhe identifydrugdruginteractionsviadeeplearningarealworldstudy AT xiaochenbo identifydrugdruginteractionsviadeeplearningarealworldstudy AT jianxiao identifydrugdruginteractionsviadeeplearningarealworldstudy |