BACKGROUND: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention ...
Drug-drug interactions (DDIs) occur when multiple medications are co-administered, potentially leading to adverse effects and compromising patient safety. However, existing DDI prediction methods often overlook the intricate interactions among chemic...
BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing ...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-being of patients, and providing essential guidance and decision support at every stage of the drug deve...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Accurate prediction of drug-drug interactions (DDIs) plays an important role in improving the efficiency of drug development and ensuring the safety of combination therapy. Most existing models rely on a single source of information to predict DDIs, ...
IEEE journal of biomedical and health informatics
Dec 5, 2024
Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Therefore, it is important to predict potential drug interactions since it can provide combination strategies of drugs...
Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug interactions (DDIs), potentially reducing efficacy and triggering adverse drug reactions (ADRs). Predicti...
Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consi...
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions predicti...
IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of...
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