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

Supervised learning models, also known as quantitative structure-activityregression (QSAR) models, are increasingly used in assisting the process ofpreclinical, small molecule drug discovery. The models are trained on dataconsisting of a finite dimensional representation of molecular structures andtheir corresponding target specific activities. These models can then be usedto predict the activity of previously unmeasured novel compounds. In this workwe address two problems related to this approach. The first is to estimate theextent to which the quality of the model predictions degrades for compoundsvery different from the compounds in the training data. The second is to adjustfor the screening dependent selection bias inherent in many training data sets.In the most extreme cases, only compounds which pass an activity-dependentscreening are reported. By using a semi-supervised learning framework, we showthat it is possible to make predictions which take into account the similarityof the testing compounds to those in the training data and adjust for thereporting selection bias. We illustrate this approach using publicly availablestructure-activity data on a large set of compounds reported by GlaxoSmithKline(the Tres Cantos AntiMalarial Set) to inhibit in vitro P. falciparum growth.

A semi-supervised learning framework for quantitative structure-activity regression modelling