The current standard method for the analysis of potential drug-drug interactions (pDDIs) is time-consuming and includes the use of multiple clinical decision support systems (CDSSs) and the interpretation by healthcare professionals. With the emergen...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40040060
Drug-target interaction (DTI) prediction speeds up drug repurposing, accelerates drug screening, and reduces drug design timeframe. Previous DTI prediction frameworks lack consideration for the multimodal nature of DTI, advanced feature representatio...
IEEE journal of biomedical and health informatics
40030413
Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-...
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...
Deep learning models have made significant progress in the biomedical field, particularly in the prediction of drug-drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs in the body, which may lead to adverse effects ...
Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with d...
Journal of chemical information and modeling
39869197
Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the me...
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 ...
IEEE journal of biomedical and health informatics
40030194
Prediction of drug-target interactions (DTIs) is one of the crucial steps for drug repositioning. Identifying DTIs through bio-experimental manners is always expensive and time-consuming. Recently, deep learning-based approaches have shown promising ...
IEEE journal of biomedical and health informatics
40030324
Drug-Drug Interactions (DDI) identification is a part of the drug safety process, that focuses at avoiding potential adverse drug effects that can lead to patient health risks. With the exponential growth in published literature, it becomes increasin...