Archive for the ‘REST’ tag
Over the last few months I’ve been writing about REST services that I’ve made available. While useful they’ve been a hassle to maintain. The main reason is that many of the Python services (such as the depiction and descriptor services) are actually front-ends to SOAP services written in Java using the CDK. Thus, I have to maintain two layers of code. While deploying services using SOAP is easy (the service code is just plain Java classes and methods deployed in a Tomcat container) writing clients is not as cross-platform as SOAP afficionados make it out to be. For example, Python clients require ZSI which is rather complex for simple services, and last time I looked, does not support certain SOAP types. While SOAPpy is an easy to use option, it is no longer maintained. Furthermore, SOAP services on their own don’t necessarily provide a RESTful interface (hence the Python frontends).
Sometime back I came across a post by Rich where he mentioned Restlets. The idea is that this package makes writing RESTful services in Java easier than going through the full blown Servlet API. Indeed, it took a day or two to refactor my SOAP based services into RESTful services using Restlet. One of the nice things about the package is that while it allows you to integrate RESTful services into a Tomcat container (say for proxying purposes) you can also deploy the services via a small web server included in the package.
What is the result of all this? The short answer is that all the CDK SOAP services that I had are now packaged into a single JAR file that can be run from the command line:
java -jar cdkrest.jar -p 6666 -s rguha.ath.cx -l services.log
You can then access services such as http://rguha.ath.cx:6666/cdk/depict/CC=O. So there’s no need to run a Tomcat instance. Furthermore, by using Restlet, the services present a RESTful interface without requiring me to write an extra layer of Python. This also allows for easy distribution – just download the JAR file and you’re all set.
Along with refactoring code for the new Restlet based approach, I’ve been documenting the services as I go along, which is better than it being scattered over multiple blog posts. The new location for all the CDK services is http://rest.rguha.net which includes downloads and documentation. Note that this site will not actually run the services – see the site for more details.
Over time other services that are not necessarily CDK specific (such as database services) will also be migrated (in fact some of the old services are simply redirected to the new service), though certain services, such as predict, will remain as a Python service.
I’ve been putting up a number of REST services for a variety of cheminformatics tasks. One that was missing was substructure searching. In many scenarios it’s useful to be able to check whether a target molecule contains a query substructure or not. This can now be done by visiting URL’s of the form
where TARGET and QUERY are SMILES and SMARTS (or SMILES) respectively (appropriately escaped). If the query pattern is found in the target molecule then the resultant page contains the string “true” otherwise it contains the string “false”. The service uses OpenBabel to perform the SMARTS matching.
Using this service, I updated the ONS data query page to allow one to filter results by SMARTS patterns. This generally only makes sense when no specific solute is selected. However, filtering all the entries in the spreadsheet (i.e., any solvent, any solute) can be slow, since each molecule is matched against the SMARTS pattern using a separate HTTP requests. This could be easily fixed using POST, but it’s a hack anyway since this type of thing should probably be done in the database (i.e., Google Spreadsheet).
The substructure search service is now updated to accept POST requests. As a result, it is possible to send in multiple SMILES strings and match them against a pattern all at one go. See the repository for a description on how to use the POST method. (The GET method is still supported but you can only match a pattern against one target SMILES). As a result, querying the ONS data using SMARTS pattens is significantly faster.
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.
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.
The current version of the REST interface to the CDK descriptors allowed one to access descriptor values for a SMILES string by simply appending it to an URL, resulting in something like
This type of URL is pretty handy to construct by hand. However, as Pat Walters pointed out in the comments to that post, SMILES containing ‘#’ will cause problems since that character is a URL fragment identifier. Furthermore, the presence of a ‘/’ in a SMILES string necessitates some processing in the service to recognize it as part of the SMILES, rather than a URL path separator. While the service could handle these (at the expense of messy code) it turned out that there were subtle bugs.
Based on Pats’ suggestion I converted the service to use base64 encoded SMILES, which let me simplify the code and remove the bugs. As a result, one cannot append the SMILES directly to the URL’s. Instead the above URL would be rewritten in the form
All the example URL’s described in my previous post that involve SMILES strings, should be rewritten using base64 encoded SMILES. So to get a document listing all descriptors for “c1ccccc1COCC” one would write
and then follow the links therein.
While this makes it a little harder to directly write out these URL’s by hand, I expect that most uses of this service would be programmatic – in which case getting base64 encoded SMILES is trivial.