Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome metabolism issues – these are just a few of the challenging tasks assigned to DMPK scientists. How can we best tackle these to ensure the success of our drug discovery projects?
Watch Optibrium CEO, Dr Matthew Segall, as he explored Optibrium’s past seven years of metabolism prediction research, and demonstrates how in silico modelling techniques can support DMPK scientists in their workflows.
The webinar covers:
- The drug metabolism challenges that might be faced by DMPK scientists
- The application of in silico modelling methods to target these challenges, including state-of-the-art quantum mechanical and machine learning models
- A demonstration of our latest software, Semeta™ , an in silico metabolism prediction platform tailored specifically for DMPK scientists
Introducing our webinar speaker
Matt Segall, PhD
CEO, Optibrium
More metabolism resources
Predicting regioselectivity of cytosolic SULT metabolism for drugs
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
Introduction to the Metabolism module
The Metabolism module enables you to accurately predict the major metabolic routes, sites, products and lability of Phase I and…
