Using the model deployment and prediction service, I put up the two linear regression models I had built so far (described in more detail here) While REST is nice, a simple web page that allows you to paste a set of SMILES and get back predictions is handy. So I whipped together a simple interface to the prediction service, allowing one to select a model, view the author-generated description and a get a nice (sortable!) table of predicted values. View it here. As noted in my previous post it’s not going to be very fast, but hopefully that will change in the future.
Posts Tagged ‘qsar’
Over the past few days I’ve been developing some predictive models in R, for the solubility data being generated as part of the ONS Solubility Challenge. As I develop the models I put up a brief summary of the results on the wiki. In the end however, we’d like to use these models to predict the solubility of untested compounds. While anybody can send me a SMILES string and get back a prediction, it’s more useful (and less work for me!) if a user can do it themselves. This requires that the models be deployed and made available as a web page or a service. Last year I developed a series of statistical web services based on R. The services were written in Java and are described in this paper. Since I’m working more with REST services these days, I wanted to see how easy it’d be to develop a model deployment system using Python, thus avoiding a multi-tiered system. With the help of rpy2, it turns out that this wasn’t very difficult.