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

We present a novel approach to tackle explainability of deep graph networksin the context of molecule property prediction tasks, named MEG (MolecularExplanation Generator). We generate informative counterfactual explanations fora specific prediction under the form of (valid) compounds with high structuralsimilarity and different predicted properties. We discuss preliminary resultsshowing how the model can convey non-ML experts with key insights into thelearning model focus in the neighborhood of a molecule.

Explaining Deep Graph Networks with Molecular Counterfactuals