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Related papers: Drug Recommendation toward Safe Polypharmacy

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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

Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory…

Computation and Language · Computer Science 2024-09-10 Zhaoyue Sun , Jiazheng Li , Gabriele Pergola , Yulan He

Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the…

Machine Learning · Computer Science 2023-01-18 Zhihang Hu , Qinze Yu , Yucheng Guo , Taifeng Wang , Irwin King , Xin Gao , Le Song , Yu Li

The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to reenter the system at a costlier…

Social and Information Networks · Computer Science 2020-01-03 Rion Brattig Correia , Luciana P. de Araújo , Mauro M. Mattos , Luis M. Rocha

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

In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent…

Quantitative Methods · Quantitative Biology 2024-07-30 Yufeng Li , Wenchao Zhao , Bo Dang , Xu Yan , Weimin Wang , Min Gao , Mingxuan Xiao

In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast, combination drug therapies can significantly improve therapeutic outcomes through…

Machine Learning · Computer Science 2026-04-24 Jiyan Song , Wenyang Wang , Chengcheng Yan , Zhiquan Han , Feifei Zhao

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

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

There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse…

Applications · Statistics 2024-05-31 Shounak Chattopadhyay , Stephanie M. Engel , David Dunson

Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug…

Computation and Language · Computer Science 2017-05-23 Sahil Manchanda , Ashish Anand

Drug synergy arises when the combined impact of two drugs exceeds the sum of their individual effects. While single-drug effects on cell lines are well-documented, the scarcity of data on drug synergy, considering the vast array of…

Quantitative Methods · Quantitative Biology 2024-04-29 Kyriakos Schwarz , Alicia Pliego-Mendieta , Amina Mollaysa , Lara Planas-Paz , Chantal Pauli , Ahmed Allam , Michael Krauthammer

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a…

Computation and Language · Computer Science 2019-11-06 Tung Tran , Ramakanth Kavuluru , Halil Kilicoglu

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug…

Quantitative Methods · Quantitative Biology 2022-02-17 Pietro Bongini , Franco Scarselli , Monica Bianchini , Giovanna Maria Dimitri , Niccolò Pancino , Pietro Liò

Drug discovery remains a slow and expensive process that involves many steps, from detecting the target structure to obtaining approval from the Food and Drug Administration (FDA), and is often riddled with safety concerns. Accurate…

Quantitative Methods · Quantitative Biology 2025-08-22 Ali Vefghi , Zahed Rahmati , Mohammad Akbari

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

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

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around…

Despite extensive safety assessments of drugs prior to their introduction to the market, certain adverse drug reactions (ADRs) remain undetected. The primary objective of pharmacovigilance is to identify these ADRs (i.e., signals). In…

Methodology · Statistics 2024-04-23 Louis Dijkstra , Tania Schink , Ronja Foraita

Drug overdose has become a public health crisis in the United States with devastating consequences. However, most of the drug overdose incidences are the consequence of recitative polysubstance usage over a defined period of time which can…

Artificial Intelligence · Computer Science 2022-04-13 Vaishali Mahipal , Mohammad Arif Ul Alam