This article is a collaboration with Intellegens, the University of Cambridge and AstraZeneca. It provides a proof-of-concept study in which Cerella™ is used to predict rat in vivo pharmacokinetic (PK) parameters and concentration–time PK profiles.
In this study, we identified a new antimalarial with an unusual structure – the only compound in the competition to be proven active, opening up new chemistry for exploration.
In this article, the team demonstrates the application of Alchemite™, a deep learning imputation method which underpins our Cerella™ technology, to physicochemical and sensory data.
OA paper outlining the practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods.
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
This article describes a novel deep learning neural network method and its application for the imputation of bioactivity data, such…
This peer-reviewed paper discusses the challenges of using uncertain experimental data to make confident decisions on the selection of compounds.…
