Date(s) - 02 Jun 2021
4:00 pm - 5:00 pm
Date: June 2, 2021
Time: 4 pm BST / 11 am EDT / 8 am PDT
Join Genentech and Optibrium for this discussion of Alchemite™, a novel deep learning approach, and its application to optimizing kinase profiling programs. Using Alchemite™ reduces the number of kinase assays required to accurately predict the full kinase selectivity profile, effectively accelerating experimental programs.
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. Furthermore, we will discuss Alchemite’s unique ability to provide reliable prediction-uncertainty-estimates that enable the selection of the most informative kinase assays and which compounds to test.
Scientist, Sr. Scientist, Program Manager, Associate Director, Director
- Matt Segall, CEO, Optibrium
- Samar Mahmoud, Senior Scientist, Optibrium
- Fabio Broccatelli, Senior Scientist, Genentech