In this study, our researchers combined an automatic model generation process for building QSAR models with the Gaussian Processes machine learning method. The article outlines the process for model construction and validation, and applies the automatic process to blood-brain barrier penetration and aqueous solubility data sets. Results are compared with ‘manually’ built models using external test sets, and show the automatic process to be highly effective for both in vivo and physico-chemical data.
O. Obrezanova, J. M. R. Gola, E. Champness, M. D. Segall, J. Comp. Aided Mol. Design, 2008, 22(6-7) pp. 431-440.
DOI: 10.1007/s10822-008-9193-8
Summary In this study, the researchers look to solve classification quantitative structure−activity relationship (QSAR) modelling problems using Gaussian processes. They…
In this demo we’re going to take a look at how StarDrop can guide the prioritisation and selections of compounds using a combination of in vitro and in silico data.
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.
