Results 331 to 340 of about 6,006,054 (377)

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction

Briefings Bioinform., 2021
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism.
Arnold K. Nyamabo, Hui Yu, Jian-Yu Shi
semanticscholar   +1 more source

MUFFIN: multi-scale feature fusion for drug-drug interaction prediction

Bioinform., 2021
MOTIVATION Adverse drug-drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients, and the society. Existing traditional machine learning
Yujie Chen, Tengfei Ma, Xixi Yang, Jianmin Wang, Bosheng Song, Xiangxiang Zeng   +5 more
semanticscholar   +1 more source

Drug–Drug Interactions

2018
A drug interaction occurs when the effects of a drug are altered by the effects of another drug, a vaccine, herb, foodstuff, or device. In drug–drug interactions, a precipitant drug increases or reduces the effects of an object drug by pharmaceutical, pharmacokinetic, or pharmacodynamic mechanisms.
Mathew George, Lincy Joseph, Sujith K
  +6 more sources

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection

IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2021
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent
Yang Qiu, Yang Zhang, Yifan Deng, Shichao Liu, Wen Zhang   +4 more
semanticscholar   +1 more source

A multimodal deep learning framework for predicting drug-drug interaction events

Bioinform., 2020
MOTIVATION Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not.
Yifan Deng, Xinran Xu, Yang Qiu, Jingbo Xia, Wen Zhang, Shichao Liu   +5 more
semanticscholar   +1 more source

CARDIOVASCULAR DRUG-DRUG INTERACTIONS

Cardiology Clinics, 2001
The drug-drug interactions discussed in this article have either documented or suspected clinical relevance for patients with cardiovascular disease and the clinician involved in the care of these patients. Oftentimes, drug-drug interactions are difficult, if not impossible, to predict because of the high degree of interpatient variability in drug ...
J R, Anderson, J J, Nawarskas
openaire   +2 more sources

Drug–drug interaction with statins

Expert Review of Clinical Pharmacology, 2008
3-hydroxy-3-methyl-glutaryl (HMG)-CoA reductase inhibitors (the so-called statins: atorvastatin, fluvastatin, pravastatin, lovastatin, rosuvastatin and simvastatin) are a well-established class of drugs in the treatment of hypercholesterolemia. Statin monotherapy is generally well tolerated, with a low frequency of adverse events.
A. Corsini, S. Bellosta
openaire   +2 more sources

Deep learning for drug-drug interaction extraction from the literature: a review

Briefings Bioinform., 2020
Drug-drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely
Tianlin Zhang, Jiaxu Leng, Y. Liu
semanticscholar   +1 more source

2020 FDA Drug-Drug Interaction Guidance -Comparison Analysis and Action Plan by Pharmaceutical Industrial Scientists.

Current drug metabolism, 2020
BACKGROUND In January 2020, the US FDA published two final guidelines, one entitled "In vitro Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry" and the other entitled "Clinical Drug ...
Sirimas Sudsakorn, Praveen M. Bahadduri, J. Fretland, Chuang Lu   +3 more
semanticscholar   +1 more source