English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The first step in drug discovery is finding drug molecule moieties withmedicinal activity against specific targets. Therefore, it is crucial toinvestigate the interaction between drug-target proteins and small chemicalmolecules. However, traditional experimental methods for discovering potentialsmall drug molecules are labor-intensive and time-consuming. There is currentlya lot of interest in building computational models to screen small drugmolecules using drug molecule-related databases. In this paper, we propose amethod for predicting drug-target binding affinity using deep learning models.This method uses a modified GRU and GNN to extract features from thedrug-target protein sequences and the drug molecule map, respectively, toobtain their feature vectors. The combined vectors are used as vectorrepresentations of drug-target molecule pairs and then fed into a fullyconnected network to predict drug-target binding affinity. This proposed modeldemonstrates its accuracy and effectiveness in predicting drug-target bindingaffinity on the DAVIS and KIBA datasets.

Drug-target affinity prediction method based on consistent expression of heterogeneous data