Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
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 …
learning models have established their usefulness in biomedical applications, especially in …
Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities
NN Wang, B Zhu, XL Li, S Liu, JY Shi… - Journal of Chemical …, 2023 - ACS Publications
Detecting drug–drug interactions (DDIs) is an essential step in drug development and drug
administration. Given the shortcomings of current experimental methods, the machine …
administration. Given the shortcomings of current experimental methods, the machine …
Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
Drug–drug interactions (DDIs) can trigger unexpected pharmacological effects on the body,
and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been …
and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been …
Network learning for biomarker discovery
Everything is connected and thus networks are instrumental in not only modeling complex
systems with many components, but also accommodating knowledge about their …
systems with many components, but also accommodating knowledge about their …
A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks
Drug–drug interactions (DDIs) prediction is a challenging task in drug development and
clinical application. Due to the extremely large complete set of all possible DDIs, computer …
clinical application. Due to the extremely large complete set of all possible DDIs, computer …
Multi-type feature fusion based on graph neural network for drug-drug interaction prediction
Abstract Background Drug-Drug interactions (DDIs) are a challenging problem in drug
research. Drug combination therapy is an effective solution to treat diseases, but it can also …
research. Drug combination therapy is an effective solution to treat diseases, but it can also …
Prediction of drug-drug interaction using an attention-based graph neural network on drug molecular graphs
YH Feng, SW Zhang - Molecules, 2022 - mdpi.com
The treatment of complex diseases by using multiple drugs has become popular. However,
drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and …
drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and …
[HTML][HTML] Improved prediction of drug-drug interactions using ensemble deep neural networks
TH Vo, NTK Nguyen, NQK Le - Medicine in Drug Discovery, 2023 - Elsevier
Nowadays, combining multiple drugs is the optimal therapy to decelerate the pathologic
process, which contains various underlying adverse effects due to drug-drug interactions …
process, which contains various underlying adverse effects due to drug-drug interactions …
[HTML][HTML] Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents
Repositioning or repurposing drugs account for a substantial part of entering approval
pipeline drugs, which indicates that drug repositioning has huge market potential and value …
pipeline drugs, which indicates that drug repositioning has huge market potential and value …
Drug-drug interactions prediction based on deep learning and knowledge graph: A review
H Luo, W Yin, J Wang, G Zhang, W Liang, J Luo, C Yan - Iscience, 2024 - cell.com
Drug-drug interactions can produce unpredictable pharmacological effects and lead to
adverse events that have the potential to cause irreversible organ damage or death …
adverse events that have the potential to cause irreversible organ damage or death …