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

The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET)properties of drug candidates are estimated to account for up to 50% of allclinical trial failures. Predicting ADMET properties has therefore been ofgreat interest to the cheminformatics and medicinal chemistry communities inrecent decades. Traditional cheminformatics approaches, whether the learner isa random forest or a deep neural network, leverage fixed fingerprint featurerepresentations of molecules. In contrast, in this paper, we learn the featuresmost relevant to each chemical task at hand by representing each moleculeexplicitly as a graph, where each node is an atom and each edge is a bond. Byapplying graph convolutions to this explicit molecular representation, weachieve, to our knowledge, unprecedented accuracy in prediction of ADMETproperties. By challenging our methodology with rigorous cross-validationprocedures and prospective analyses, we show that deep featurization betterenables molecular predictors to not only interpolate but also extrapolate tonew regions of chemical space.

Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization