Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …
process of drug discovery. There is a need to develop novel and efficient prediction …
How can synergism of traditional medicines benefit from network pharmacology?
H Yuan, Q Ma, H Cui, G Liu, X Zhao, W Li, G Piao - Molecules, 2017 - mdpi.com
Many prescriptions of traditional medicines (TMs), whose efficacy has been tested in clinical
practice, have great therapeutic value and represent an excellent resource for drug …
practice, have great therapeutic value and represent an excellent resource for drug …
Drug repositioning based on the heterogeneous information fusion graph convolutional network
In silico reuse of old drugs (also known as drug repositioning) to treat common and rare
diseases is increasingly becoming an attractive proposition because it involves the use of de …
diseases is increasingly becoming an attractive proposition because it involves the use of de …
[HTML][HTML] NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
Results Inspired by recent advance of information passing and aggregation techniques that
generalize the convolution neural networks to mine large-scale graph data and greatly …
generalize the convolution neural networks to mine large-scale graph data and greatly …
A review of network-based approaches to drug repositioning
Experimental drug development is time-consuming, expensive and limited to a relatively
small number of targets. However, recent studies show that repositioning of existing drugs …
small number of targets. However, recent studies show that repositioning of existing drugs …
[HTML][HTML] Artificial intelligence in pharmaceutical sciences
Drug discovery and development affects various aspects of human health and dramatically
impacts the pharmaceutical market. However, investments in a new drug often go …
impacts the pharmaceutical market. However, investments in a new drug often go …
Biomedical data and computational models for drug repositioning: a comprehensive review
Drug repositioning can drastically decrease the cost and duration taken by traditional drug
research and development while avoiding the occurrence of unforeseen adverse events …
research and development while avoiding the occurrence of unforeseen adverse events …
[HTML][HTML] Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines
Highlights•Understanding the existing drug repositioning methods with a top-down
flowchart.•Prioritizing repositioning methods using their integrated knowledge and …
flowchart.•Prioritizing repositioning methods using their integrated knowledge and …
DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
Motivation Finding computationally drug–target interactions (DTIs) is a convenient strategy
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …
development. Computational prediction of DTIs can effectively complement experimental …