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Drug Interactions

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EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information.

Briefings in bioinformatics
MOTIVATION: The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the l...

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction.

Briefings in bioinformatics
MOTIVATION: Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug-protein interactions mainly focus on ...

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

Briefings in bioinformatics
One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available pred...

Drug-target interaction predication via multi-channel graph neural networks.

Briefings in bioinformatics
Drug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning meth...

Machine learning methods, databases and tools for drug combination prediction.

Briefings in bioinformatics
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In ...

Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

Combinatorial chemistry & high throughput screening
BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interac...

Comparative analysis of molecular fingerprints in prediction of drug combination effects.

Briefings in bioinformatics
Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computa...

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

Briefings in bioinformatics
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computat...

SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.

Bioinformatics (Oxford, England)
MOTIVATION: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an o...

Deep learning identifies synergistic drug combinations for treating COVID-19.

Proceedings of the National Academy of Sciences of the United States of America
Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role i...