Posts Tagged ‘solubility’

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.

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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.


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In a previous post, I described a simple web form to query and visualize the solubility data being generated as part of the ONS Challenge. The previous approach required me to manually download the data and load it into a Postgres database. While trivial from a coding point of view, it’s a pain since I have to keep my local DB in sync with the Google Docs spreadsheet.

However, Google comes to the rescue with their Query API, which allows us to view the spreadsheet as a table which can be queried using an SQL like language. As a result, I can ditch the whole local database, and simply have an HTML page constructed using Javascript, which performs queries directly on the solubility spreadsheet.

This is very nice since I now no longer have to maintain a local DB and ensure that it’s in sync with Jean-Claudes results. Of course, there are some drawbacks to this method. First, the query page will assume that the data in the spreadsheet is clean. So if there are two entries called “Ethanol” and “ethanol”, they will be considered seperate solvents. Secondly, this approach cannot be used to include cheminformatics in the queries, since Google doesn’t support that functionality. Finally, it’s not going to be very good for large spreadsheets.

However, this is a very nice API, that allows one to elegantly integrate web applications with live data. I heart Google!

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There was a FriendFeed dicussion on the use of RDF triples for representing the solubilty data generated by Jean-Claude and others as part of the ONS Solubility Challenge. Part of the discussion revolved around letting RDF novices easily perform queries of the data being collected.  Not knowing much about RDF, I took the raw data from the Google Docs and loaded it into a Postgres database and whipped up a simple query form.

The DB and form are nothing remarkable. But what is cool is that the Google Visualization API makes it really easy for me include charts and other visualizations very easily. For example, if you select “any” as the solvent and then select a solute, the form creates a table of solubilities of that solute in all the solvents it was measured in. A natural view of the data is to look at a bar chart of the solubilities across the various solvents.

Since my form is built using mod_python, it’s a simple matter to write out the Javascript to call the Google API. After some boilerplate code, all that needs to be done is to create a DataTable object, set the column types and names and then populate it. See here for example code, which I modified.

var data = new google.visualization.DataTable();
data.addColumn(’string’, ‘Solvent’);
data.addColumn(’number’, ‘Conc (M)’);
data.setValue(0, 0, ‘thf’);
data.setValue(0, 1, 1.23);
data.setValue(1, 0, ‘acetonitrile’);
data.setValue(1, 1, 2.34);

Once you have the data all stored, some more boilerplate code allows us to easily insert the chart into the final web page. Very neat!

(Of course, since these queries do not involve chemistry / cheminformatics, I could skip Python and Postgres and simply do the whole thing in Javascript, querying the Google Docs spreadsheet directly. This means that the results from the form would always be in sync with the Google Doc, but that’s for another evening)

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