Skip to main content

Accessing web services with cURL


ChEMBL web services are really friendly. We provide live online documentation, support for CORS and JSONP techniques to support web developers in creating their own web widgets. For Python developers, we provide dedicated client library as well as examples using the client and well known requests library in a form of ipython notebook. There are also examples for Java and Perl, you can find it here.

But this is nothing for real UNIX/Linux hackers. Real hackers use cURL. And there is a good reason to do so. cURL comes preinstalled on many Linux distributions as well as OSX. It follows Unix philosophy and can be joined with other tools using pipes. Finally, it can be used inside bash scripts which is very useful for automating tasks.

Unfortunately first experiences with cURL can be frustrating. For example, after studying cURL manual pages, one may think that following will return set of compounds in json format:


But the result is quite dissapointing...


The reason is that --data-urlencode (-d) tells our server (by setting Content-Type header) that this request parameters are encoded in "application/x-www-form-urlencoded" - the default Internet media type. In this format, each key-value pair is separated by an '&' character, and each key is separated from its value by an '=' character for example:


This is not the format we used. We provided our data in JSON format, so how do we tell the ChEMBL servers the format we are using? It turns out it is quite simple, we just need to specify a Content-Type header:


If we would like to omit the header, correct invocation would be:


OK, so request parameters can be encoded as key-value pairs (default) or JSON (header required). What about result format? Currently, ChEMBL web services support JSON and XML output formats. How do we choose the format we would like the results to be returned as? This can be done in three ways:

1. Default - if you don't do anything to indicate desired output format, XML will be assumed. So this:


will produce XML.

2. Format extension - you can append format extension (.xml or .json) to explicitly state your desired format:


will produce JSON.

3. `Accept` Header - this header specifies Content-Types that are acceptable for the response, so:


will produce JSON.

Enough boring stuff - Lets write a script!


Scripts can help us to automate repetitive tasks we have to perform. One example of such a task would be retrieving a batch of first 100 compounds (CHEMBL1 to CHEMBL100). This is very easy to code with bash using curl (Note the usage of the -s parameter, which prevents curl from printing out network and progress information):


Executing this script will return information about first 100 compounds in JSON format. But if you carefully inspect the returned output you will find that some compound identifiers don't exist in ChEMBL:


We need to add some error handling, for example checking if HTTP status code returned by server is equal to 200 (OK). Curl comes with --fail (-f) option, which tells it to return non-zero exit code if response is not valid. With this knowledge we can modify our script to add error handling:


OK, but the output still looks like a chaotic soup of strings and brackets, and is not very readable...

Usually we would use a classic trick to pretty print json - piping it through python:


But it won't work in our case:



Why? The reason is that python trick can pretty-print a single JSON document. And what we get as the output is a collection of JSON documents, each of which describes different compound and is written in separate line. Such a format is called Line Delimited JSON and is very useful and well known.

Anyway, we are data scientists after all so we know a plenty of other tools that can help. In this case the most useful is jq - "lightweight and flexible command-line JSON processor", kind of sed for JSON.

With jq it's very easy to pretty print our script output:



Great, so we finally can really see what we have returned from a server. Let's try to extract some data from our JSON collection, let it be chemblId and molecular weight:



Perfect, can we have both properties printed in one line and separated by tab? Yes, we can!



So now we can get the ranking of first 100 compounds sorted by their weight:




Exercises for readers:

1. Can you modify compounds.sh script to accept a range (first argument is start, second argument is length) of compounds?
2. Can you modify the script to read compound identifiers from a file?
3. Can you add a 'structure' parameter, which accepts a SMILES string. When this 'structure' parameter is present, the script will return similar compounds (you can decide on the similarity cut off or add an extra parameter)?



Comments

Popular posts from this blog

Here's a nice Christmas gift - ChEMBL 35 is out!

Use your well-deserved Christmas holidays to spend time with your loved ones and explore the new release of ChEMBL 35!            This fresh release comes with a wealth of new data sets and some new data sources as well. Examples include a total of 14 datasets deposited by by the ASAP ( AI-driven Structure-enabled Antiviral Platform) project, a new NTD data se t by Aberystwyth University on anti-schistosome activity, nine new chemical probe data sets, and seven new data sets for the Chemogenomic library of the EUbOPEN project. We also inlcuded a few new fields that do impr ove the provenance and FAIRness of the data we host in ChEMBL:  1) A CONTACT field has been added to the DOCs table which should contain a contact profile of someone willing to be contacted about details of the dataset (ideally an ORCID ID; up to 3 contacts can be provided). 2) In an effort to provide more detailed information about the source of a deposited dat...

Improvements in SureChEMBL's chemistry search and adoption of RDKit

    Dear SureChEMBL users, If you frequently rely on our "chemistry search" feature, today brings great news! We’ve recently implemented a major update that makes your search experience faster than ever. What's New? Last week, we upgraded our structure search engine by aligning it with the core code base used in ChEMBL . This update allows SureChEMBL to leverage our FPSim2 Python package , returning results in approximately one second. The similarity search relies on 256-bit RDKit -calculated ECFP4 fingerprints, and a single instance requires approximately 1 GB of RAM to run. SureChEMBL’s FPSim2 file is not currently available for download, but we are considering generating it periodicaly and have created it once for you to try in Google Colab ! For substructure searches, we now also use an RDKit -based solution via SubstructLibrary , which returns results several times faster than our previous implementation. Additionally, structure search results are now sorted by...

ChEMBL 34 is out!

We are delighted to announce the release of ChEMBL 34, which includes a full update to drug and clinical candidate drug data. This version of the database, prepared on 28/03/2024 contains:         2,431,025 compounds (of which 2,409,270 have mol files)         3,106,257 compound records (non-unique compounds)         20,772,701 activities         1,644,390 assays         15,598 targets         89,892 documents Data can be downloaded from the ChEMBL FTP site:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/ Please see ChEMBL_34 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_34/chembl_34_release_notes.txt New Data Sources European Medicines Agency (src_id = 66): European Medicines Agency's data correspond to EMA drugs prior to 20 January 2023 (excluding ...

Improved querying for SureChEMBL

    Dear SureChEMBL users, Earlier this year we ran a survey to identify what you, the users, would like to see next in SureChEMBL. Thank you for offering your feedback! This gave us the opportunity to have some interesting discussions both internally and externally. While we can't publicly reveal precisely our plans for the coming months (everything will be delivered at the right time), we can at least say that improving the compound structure extraction quality is a priority. Unfortunately, the change won't happen overnight as reprocessing 167 millions patents takes a while. However, the good news is that the new generation of optical chemical structure recognition shows good performance, even for patent images! We hope we can share our results with you soon. So in the meantime, what are we doing? You may have noticed a few changes on the SureChEMBL main page. No more "Beta" flag since we consider the system to be stable enough (it does not mean that you will never ...

ChEMBL brings drug bioactivity data to the Protein Data Bank in Europe

In the quest to develop new drugs, understanding the 3D structure of molecules is crucial. Resources like the Protein Data Bank in Europe (PDBe) and the Cambridge Structural Database (CSD) provide these 3D blueprints for many biological molecules. However, researchers also need to know how these molecules interact with their biological target – their bioactivity. ChEMBL is a treasure trove of bioactivity data for countless drug-like molecules. It tells us how strongly a molecule binds to a target, how it affects a biological process, and even how it might be metabolized. But here's the catch: while ChEMBL provides extensive information on a molecule's activity and cross references to other data sources, it doesn't always tell us if a 3D structure is available for a specific drug-target complex. This can be a roadblock for researchers who need that structural information to design effective drugs. Therefore, connecting ChEMBL data with resources like PDBe and CSD is essen...