Creating open source Bayesian models with a big dataset
What is it about?
We use open source fingerprints and a Bayesian algorithm to build thousands of computational models from data in a very big public dataset called ChEMBL. We demonstrate the cross validation of these models, make them openly accessible and demonstrate how they can be imported in to a mobile app and used for predictions.
Why is it important?
We are not aware of anyone using ChEMBL in this way with open source technologies and making the thousands of models accessible. In addition we describe a novel algorithm for detecting thresholds for active / inactive in continuous data. Finally we access the effect of folding on the fingerprints.
The following have contributed to this page: Dr Sean Ekins and Alex Michael Clark