Overcoming challenges in drug metabolism: in silico approaches
Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome…
This article discusses Quantitative Structure – Activity relationships (QSAR) methods to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) properties.
It covers statistical modelling techniques, molecular descriptors and data sets used for model building, alongside the application of predictive ADMET models in drug discovery, and challenges faced.
J. M. Gola, O. Obrezanova, E. Champness, M. D. Segall, QSAR Comb. Sci., 2006, 25(12), pp. 1172 – 1180
DOI: 10.1002/qsar.200610093
Property prediction is one of the key uses of our StarDrop suite of integrated software. Discover modules such as ADME QSAR, Auto-Modeller and Metabolism module to find out more about how StarDrop supports ADMET property prediction.
Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome…
In this webinar, we examine the effective use of QSAR modelling in drug discovery and discuss a variety of pain points for medicinal chemists in knowing when a model can be trusted and how to avoid common pitfalls.
During this example we will consider three compounds from a lead series which we would like to try to evolve into a candidate. The compound has a good profile of ADME properties but insufficient inhibition of the target, the Serotonin transporter. In this example we will use StarDrop’s Nova module to generate new ideas for compounds to improve the potency while maintaining the balance of other properties.