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

Latent representations of drugs and their targets produced by contemporarygraph autoencoder models have proved useful in predicting many types ofnode-pair interactions on large networks, including drug-drug, drug-target, andtarget-target interactions. However, most existing approaches model either thenode's latent spaces in which node distributions are rigid or do noteffectively capture the interrelations between drugs; these limitations hinderthe methods from accurately predicting drug-pair interactions. In this paper,we present the effectiveness of variational graph autoencoders (VGAE) inmodeling latent node representations on multimodal networks. Our approach canproduce flexible latent spaces for each node type of the multimodal graph; theembeddings are used later for predicting links among node pairs under differentedge types. To further enhance the models' performance, we suggest a new methodthat concatenates Morgan fingerprints, which capture the molecular structuresof each drug, with their latent embeddings before preceding them to thedecoding stage for link prediction. Our proposed model shows competitiveresults on three multimodal networks: (1) a multimodal graph consisting of drugand protein nodes, (2) a multimodal graph constructed from a subset of theDrugBank database involving drug nodes under different interaction types, and(3) a multimodal graph consisting of drug and cell line nodes. Our source codeis publicly available at https://github.com/HySonLab/drug-interactions.

Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs