Imputation of sensory properties using deep learning
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.
In this webinar, Jeff Blaney (Senior Director of Discovery Chemistry, Genentech), Darren Green (Head of Cheminformatics & Data Science, GlaxoSmithKline), Julian Levell (Head of Discovery, New Equilibrium Biosciences), and Matthew Segall (CEO, Optibrium) discuss the state of AI in early drug discovery from hit to preclinical candidate.
The group share their experiences with and expectations of AI, including predictive modelling, synthesis prediction, and generative chemistry.
Hear about the successes of AI drug discovery and an outlook on what AI needs to achieve to really transform the industry.
More AI-guided discovery resources
Real world case studies: Predicting pharmacokinetics from limited ADME data with deep learning
Now, watch Matt Segall, PhD, CEO at Optibrium, as he introduces a real world case study where we applied deep learning to guide a project, in which potential compounds were displaying good activity profiles but the team wanted to improve their PK profile to achieve better efficacy.
Practical applications of deep learning to impute heterogeneous drug discovery data
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
