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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Graphs are ubiquitous data structures for representing interactions betweenentities. With an emphasis on the use of graphs to represent chemicalmolecules, we explore the task of learning to generate graphs that conform to adistribution observed in training data. We propose a variational autoencodermodel in which both encoder and decoder are graph-structured. Our decoderassumes a sequential ordering of graph extension steps and we discuss andanalyze design choices that mitigate the potential downsides of thislinearization. Experiments compare our approach with a wide range of baselineson the molecule generation task and show that our method is more successful atmatching the statistics of the original dataset on semantically importantmetrics. Furthermore, we show that by using appropriate shaping of the latentspace, our model allows us to design molecules that are (locally) optimal indesired properties.

Constrained Graph Variational Autoencoders for Molecule Design