Optimising Kinase Profiling Programmes with Deep Learning
This webinar, hosted by the Pistoia Alliance, was presented by Fabio Broccatelli from Genentech, and Samar Mahmoud & Matt Segall from Optibrium.
In this webinar, we will discuss Alchemite™, a novel deep learning approach, and its application to optimising kinase profiling programmes.
The team will demonstrate the method’s performance on a data set of approximately 650 kinases and 10,000 compounds, significantly outperforming state-of-the-art quantitative structure-activity relationship (QSAR) approaches, including multi-target deep learning.
Many computational approaches have been explored to focus kinase screening programmes on key kinases while narrowing down the number of compounds and assays to run. However, they struggle with a large number of kinases and the limitation that only a relatively small number of compounds have been measured in any assay. The team will illustrate Alchemite’s unique ability to learn the interrelationships between the different kinase assays, building a model that combines the data from all considered kinases. It effectively utilises the sparse data sets available and pinpoints key kinases to predict a compound’s full kinase profile.