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Drug Interactions

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Phar-LSTM: a pharmacological representation-based LSTM network for drug-drug interaction extraction.

PeerJ
Pharmacological drug interactions are among the most common causes of medication errors. Many different methods have been proposed to extract drug-drug interactions from the literature to reduce medication errors over the last few years. However, the...

A simplified similarity-based approach for drug-drug interaction prediction.

PloS one
Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learni...

DMFDDI: deep multimodal fusion for drug-drug interaction prediction.

Briefings in bioinformatics
Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and cl...

An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network.

Computers in biology and medicine
Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, wh...

Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network.

Nature computational science
Drug-drug interactions (DDIs) for emerging drugs offer possibilities for treating and alleviating diseases, and accurately predicting these with computational methods can improve patient care and contribute to efficient drug development. However, man...

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities.

Journal of chemical information and modeling
Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensi...

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction.

Trends in pharmacological sciences
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are crit...

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors.

Journal of chemical information and modeling
Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essent...

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated proce...

PEB-DDI: A Task-Specific Dual-View Substructural Learning Framework for Drug-Drug Interaction Prediction.

IEEE journal of biomedical and health informatics
Adverse drug-drug interactions (DDIs) pose potential risks in polypharmacy due to unknown physicochemical incompatibilities between co-administered drugs. Recent studies have utilized multi-layer graph neural network architectures to model hierarchic...