AIMC Topic: Drug Interactions

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STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions.

Briefings in bioinformatics Jul 18, 2022
Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures of d...
Drug-Related Side Effects and Adverse Reactions Data Collection Drug Interactions Humans Software Neural Networks, Computer
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A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions.

Briefings in bioinformatics Jul 18, 2022
Predicting drug-target interactions (DTIs) is crucial at many phases of drug discovery and repositioning. Many computational methods based on heterogeneous networks (HNs) have proved their potential to predict DTIs by capturing extensive biological k...
Drug Interactions Semantics Drug Discovery Neural Networks, Computer
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SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.

Bioinformatics (Oxford, England) Jun 24, 2022
MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to exp...
Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Drug Interactions Humans
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BioDKG-DDI: predicting drug-drug interactions based on drug knowledge graph fusing biochemical information.

Briefings in functional genomics May 21, 2022
The way of co-administration of drugs is a sensible strategy for treating complex diseases efficiently. Because of existing massive unknown interactions among drugs, predicting potential adverse drug-drug interactions (DDIs) accurately is promotive t...
Humans Neural Networks, Computer Drug Interactions Pattern Recognition, Automated Pharmaceutical Preparations Reproducibility of Results
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3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction.

Briefings in bioinformatics May 13, 2022
MOTIVATION: Drug-drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificia...
Neural Networks, Computer Humans Drug-Related Side Effects and Adverse Reactions Artificial Intelligence Software Drug Interactions
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Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions.

Briefings in bioinformatics May 13, 2022
Drug-drug interactions (DDIs) are known as the main cause of life-threatening adverse events, and their identification is a key task in drug development. Existing computational algorithms mainly solve this problem by using advanced representation lea...
Neural Networks, Computer Pattern Recognition, Automated Knowledge Bases Drug Interactions Algorithms
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DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.

Briefings in bioinformatics May 13, 2022
Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experime...
Drug Interactions Drug Development Polymers Drug Repositioning Neural Networks, Computer
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BridgeDPI: a novel Graph Neural Network for predicting drug-protein interactions.

Bioinformatics (Oxford, England) Apr 28, 2022
MOTIVATION: Exploring drug-protein interactions (DPIs) provides a rapid and precise approach to assist in laboratory experiments for discovering new drugs. Network-based methods usually utilize a drug-protein association network and predict DPIs by t...
Drug Interactions Reproducibility of Results Humans Proteins Neural Networks, Computer Software
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Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities.

Briefings in bioinformatics Mar 10, 2022
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 are more and more popular in recent y...
Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Humans Proteins Drug Discovery Drug Interactions
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ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

Briefings in bioinformatics Mar 10, 2022
MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds...
Algorithms Neural Networks, Computer Drug Interactions Drug Development Proteins
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