Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility
O. Obrezanova, J. M. R. Gola, E. Champness, M. D. Segall, J. Comp. Aided Mol. Design, 2008, 22(6-7) pp. 431-440.
In this article, we present an automatic model generation process for building QSAR models combined with Gaussian Processes, a powerful machine learning modeling method. We describe the stages of the process that ensure models are built and validated within a rigorous framework: descriptor calculation, splitting data into training, validation and test sets, descriptor filtering, application of modeling techniques and selection of the best model. We apply this automatic process to data sets of blood-brain barrier penetration and aqueous solubility data sets and compare the resulting automatically generated models with ‘manually’ built models using external test sets. The results demonstrate the effectiveness of the automatic model generation process for two types of data sets commonly encountered in building ADME QSAR models, a small set of in vivo data and a large set of physico-chemical data.
You can read a copy of this automatic QSAR modeling article as a PDF file via the button below. Alternatively, visit the journal webpage to view the final published version of the article.
INTERESTED IN DRUG DISCOVERY?
With its comprehensive suite of integrated software, StarDrop™ delivers best-in-class in silico technologies within a highly visual and user-friendly interface. StarDrop™ enables a seamless flow from the latest data through predictive modelling to decision-making regarding the next round of synthesis and research, improving the speed, efficiency, and productivity of the drug optimisation and discovery process.