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Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking…

Machine Learning · Computer Science 2025-06-03 Tung-Lam Ngo , Ba-Hoang Tran , Duy-Cat Can , Trung-Hieu Do , Oliver Y. Chén , Hoang-Quynh Le

Drug-Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety. As experimental discovery is resource-intensive and time-consuming, efficient computational methodologies have become essential. The predominant…

Machine Learning · Computer Science 2026-02-03 Xinmo Jin , Bowen Fan , Xunkai Li , Henan Sun , YuXin Zeng , Zekai Chen , Yuxuan Sun , Jia Li , Qiangqiang Dai , Hongchao Qin , Rong-Hua Li , Guoren Wang

Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI, an application for browsing evidence of supplement-drug…

Computation and Language · Computer Science 2020-07-08 Lucy Lu Wang , Oyvind Tafjord , Arman Cohan , Sarthak Jain , Sam Skjonsberg , Carissa Schoenick , Nick Botner , Waleed Ammar

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very…

Social and Information Networks · Computer Science 2016-01-15 Rion Brattig Correia , Lang Li , Luis M. Rocha

Drug-drug interactions (DDI) can cause severe adverse drug reactions and pose a major challenge to medication therapy. Recently, informatics-based approaches are emerging for DDI studies. In this paper, we aim to identify key…

Quantitative Methods · Quantitative Biology 2019-12-09 Jianyuan Deng , Fusheng Wang

Drug-drug interactions (DDIs) represent a critical challenge in pharmacology, often leading to adverse drug reactions with significant implications for patient safety and healthcare outcomes. While graph-based methods have achieved strong…

Machine Learning · Computer Science 2025-07-15 Mengjie Chen , Ming Zhang , Cunquan Qu

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these…

Computers and Society · Computer Science 2015-07-21 Juan M. Banda , Tobias Kuhn , Nigam H. Shah , Michel Dumontier

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in…

Polypharmacy, defined as the use of multiple drugs together, is a standard treatment method, especially for severe and chronic diseases. However, using multiple drugs together may cause interactions between drugs. Drug-drug interaction…

Machine Learning · Computer Science 2022-07-13 Farhan Tanvir , Khaled Mohammed Saifuddin , Esra Akbas

When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction. Interaction between drugs may have a negative impact on patients or…

Computation and Language · Computer Science 2020-08-31 Siliang Tang , Qi Zhang , Tianpeng Zheng , Mengdi Zhou , Zhan Chen , Lixing Shen , Xiang Ren , Yueting Zhuang , Shiliang Pu , Fei Wu

Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a…

Artificial Intelligence · Computer Science 2018-03-13 Meng Wang

Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress…

Machine Learning · Computer Science 2025-02-11 Zhaoying Wang , Yingdan Shi , Xiang Liu , Can Chen , Jun Wen , Ren Wang

Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to co-administer two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly combined use. In recent years, automatically…

Computation and Language · Computer Science 2017-05-19 Zibo Yi , Shasha Li , Jie Yu , Qingbo Wu

Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual representation of the drug structures. We present the first…

Machine Learning · Computer Science 2021-03-22 Devendra Singh Dhami , Siwen Yan , Gautam Kunapuli , David Page , Sriraam Natarajan

Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships…

Machine Learning · Computer Science 2025-08-27 Hongbo Liu , Siyi Li , Zheng Yu

Interference between pharmacological substances can cause serious medical injuries. Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases but can also result in a reduction of drug development cost.…

Machine Learning · Computer Science 2019-08-06 Md. Rezaul Karim , Michael Cochez , Joao Bosco Jares , Mamtaz Uddin , Oya Beyan , Stefan Decker

Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even…

Machine Learning · Computer Science 2023-03-07 Tian Bian , Yuli Jiang , Jia Li , Tingyang Xu , Yu Rong , Yi Su , Timothy Kwok , Helen Meng , Hong Cheng

Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown adverse effects…

Computation and Language · Computer Science 2024-03-14 Fangqi Zhu , Yongqi Zhang , Lei Chen , Bing Qin , Ruifeng Xu

It is a common practice in modern medicine to prescribe multiple medications simultaneously to treat diseases. However, these medications could have adverse reactions between them, known as Drug-Drug Interactions (DDI), which have the…

Machine Learning · Computer Science 2024-12-10 Azwad Tamir , Jiann-Shiun Yuan

Drug-drug interactions (DDIs) are a leading cause of preventable adverse events, often complicating treatment and increasing healthcare costs. At the same time, knowing which drugs do not interact is equally important, as such knowledge…

Machine Learning · Computer Science 2026-01-08 Maryam Abdollahi Shamami , Babak Teimourpour , Farshad Sharifi
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