Skip to main content

Beaker now officially part of ChEMBL web services


We have mentioned Beaker (a.k.a the ChEMBL cheminformatics utility web service), several times on the blog (here, here and here), but have not devoted an entire post to Beaker. Well, here it is.

Beaker - what's this?


It's a small utility, that makes chemistry software available securely over https. You no longer need to install a chemical toolkit in order to convert your molfile to SMILES or calculate descriptors. If you have an internet connection (if you can read this, chances are you do), you can use Beaker. We recommend you head over to the interactive online documentation (https://www.ebi.ac.uk/chembl/api/utils/docs), to see the full list of functionality it offers and try it with your own data.

Which toolkits are used by Beaker?

Under-the-hood Beaker is exposing the functionality of the RDKit cheminformatics library. Beaker's optical structure recognition methods use the OSRA library.

 

Do I need an API Key?


As long as you are making no more than 1 request per second, you do not need an API key. Beaker provides standard set of response headers to inform about rate limiting:



There is also one custom header:



This lets you know how you have been authenticated. The default authentication is IP-based, which means that if any other person uses Beaker from the same IP, it will affect your rate limit. This is why having your own API key can be useful - no one can 'steal' your rate limit and it will be slightly higher than default as well. If you need a key, just write to us.

I tried it and it doesn't work...


Before contacting us and submitting bug report, please try a few things first:

1. Submit data via https://www.ebi.ac.uk/chembl/api/utils/docs. It it works there, see point 2.
2. Check data encoding. Unlike the ChEMBL data web services (where you should use percent encoding as described in the previous blog post), if you are accessing Beaker via GET, then all data provided should be base64 encoded. This is why, if you want to use GET to convert 'CCC' SMILES to molfile this link won't work:  
https://www.ebi.ac.uk/chembl/api/utils/smiles2ctab/CCC.
'CCC' has to be base64 encoded first and  
base64('CCC') == 'Q0NDQw==',
so the valid link is https://www.ebi.ac.uk/chembl/api/utils/smiles2ctab/Q0NDQw==. Our online documentation will do encoding for you, and present what URL was really executed:


3. Use POST where possible. GET requests are nice, because everything gets included into URL, so you can embed such a URL in a blogpost, like we just did. One issue with GET is that there is often a maximum number of characters you can send, although this does depend on server setup. If you would like to use Beaker from ChEMBL servers for example, your link can't exceed 4000 characters. Base64 encoding will make any parameter about 1/3 longer. So for example, if you would like to send an image, in order to perform Optical Structure Recognition (OSRA), it's very hard to find a valid, good quality image, that is less than 1.2 Kb in size, so in that case using GET is not a good idea. Also, do not forget you can use curl to submit your POST requests. Below we provide some examples of how to access Beaker via POST with curl:



4. If using GET, check what type of base64 are you using. Standard implementation of base64 use the following characters:
[a-zA-Z0-9+/]
 Those two last signs ('+' and '/'), are not url-safe as they have special meaning in URLs. This is why Beaker uses url-safe version which substitutes '-' instead of '+' and '_' instead of '/' in the standard base64 alphabet. For reference, please click here.

Does ChEMBL python client library work with Beaker?

Yes, and even more it adds enough syntactic sugar to make it feel like your are using locally installed chemical toolkit. For example, look how easy it is, to compute maximum common substructure from three compounds, given as SMILES strings:


You can install the python client library by using 'pip install chembl_webresource_client' or download it here and expect more examples in a future blog post.

Does it work without the client?

Of course it does. You have already seen an example of how to use Beaker from JavaScript (using the online documentation) and python (using the client library). But because curl is very common tool, available on many platforms, you can execute calls to Beaker from your command line in bash. Bash has a very cool feature called pipes, so you can chain the output of one command to the input of another. This way you can mix calls to our data web services with Beaker calls. As an example let's assume that you have a photo of a compound. This could be a scan of the paper document, such as patent or a photo of a conference poster taken using your mobile phone, but it has to have decent resolution and quality:


Original image available here

If we would like to find the compound in ChEMBL that is most similar to the one recognized from the image above, we could use this line of bash script:



The script may look a bit hackish, but this is because we wanted to only use standard command line tools, that can be found on OSX and Linux systems. In production, we would never use grep, sed and awk to parse JSON because this is bad (instead we encourage you to try jq), but we wanted to show a nice example of using pipes to combine different tools. Anyway, the end result of running this command will be open the following page in your browser: https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL2107150.


Is Beaker open source software, can I see the code?

Yes, it's hosted on GitHub (https://github.com/chembl/chembl_beaker) under a Apache 2.0 license and the latest stable version is always registered in PyPI.

This also means that you can deploy your own local Beaker version. Reasons why you might like to do this include:

1. You don't want to rely on availability of ChEMBL web services or care about rate limiting.
2. You don't want to send proprietary compounds to a public service.
3. You would prefer to install your own chemical toolkit (on only 1 machine), and access its services over http(s).

How do I cite Beaker?


Please use the following publication:




If you have any questions about Beaker or any other ChEMBL services, please let us know.

Comments

Popular posts from this blog

New SureChEMBL announcement

(Generated with DALL-E 3 ∙ 30 October 2023 at 1:48 pm) We have some very exciting news to report: the new SureChEMBL is now available! Hooray! What is SureChEMBL, you may ask. Good question! In our portfolio of chemical biology services, alongside our established database of bioactivity data for drug-like molecules ChEMBL , our dictionary of annotated small molecule entities ChEBI , and our compound cross-referencing system UniChem , we also deliver a database of annotated patents! Almost 10 years ago , EMBL-EBI acquired the SureChem system of chemically annotated patents and made this freely accessible in the public domain as SureChEMBL. Since then, our team has continued to maintain and deliver SureChEMBL. However, this has become increasingly challenging due to the complexities of the underlying codebase. We were awarded a Wellcome Trust grant in 2021 to completely overhaul SureChEMBL, with a new UI, backend infrastructure, and new f

A python client for accessing ChEMBL web services

Motivation The CheMBL Web Services provide simple reliable programmatic access to the data stored in ChEMBL database. RESTful API approaches are quite easy to master in most languages but still require writing a few lines of code. Additionally, it can be a challenging task to write a nontrivial application using REST without any examples. These factors were the motivation for us to write a small client library for accessing web services from Python. Why Python? We choose this language because Python has become extremely popular (and still growing in use) in scientific applications; there are several Open Source chemical toolkits available in this language, and so the wealth of ChEMBL resources and functionality of those toolkits can be easily combined. Moreover, Python is a very web-friendly language and we wanted to show how easy complex resource acquisition can be expressed in Python. Reinventing the wheel? There are already some libraries providing access to ChEMBL d

LSH-based similarity search in MongoDB is faster than postgres cartridge.

TL;DR: In his excellent blog post , Matt Swain described the implementation of compound similarity searches in MongoDB . Unfortunately, Matt's approach had suboptimal ( polynomial ) time complexity with respect to decreasing similarity thresholds, which renders unsuitable for production environments. In this article, we improve on the method by enhancing it with Locality Sensitive Hashing algorithm, which significantly reduces query time and outperforms RDKit PostgreSQL cartridge . myChEMBL 21 - NoSQL edition    Given that NoSQL technologies applied to computational chemistry and cheminformatics are gaining traction and popularity, we decided to include a taster in future myChEMBL releases. Two especially appealing technologies are Neo4j and MongoDB . The former is a graph database and the latter is a BSON document storage. We would like to provide IPython notebook -based tutorials explaining how to use this software to deal with common cheminformatics p

Multi-task neural network on ChEMBL with PyTorch 1.0 and RDKit

  Update: KNIME protocol with the model available thanks to Greg Landrum. Update: New code to train the model and ONNX exported trained models available in github . The use and application of multi-task neural networks is growing rapidly in cheminformatics and drug discovery. Examples can be found in the following publications: - Deep Learning as an Opportunity in VirtualScreening - Massively Multitask Networks for Drug Discovery - Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set But what is a multi-task neural network? In short, it's a kind of neural network architecture that can optimise multiple classification/regression problems at the same time while taking advantage of their shared description. This blogpost gives a great overview of their architecture. All networks in references above implement the hard parameter sharing approach. So, having a set of activities relating targets and molecules we can tra

Using ChEMBL activity comments

We’re sometimes asked what the ‘activity_comments’ in the ChEMBL database mean. In this Blog post, we’ll use aspirin as an example to explain some of the more common activity comments. First, let’s review the bioactivity data included in ChEMBL. We extract bioactivity data directly from   seven core medicinal chemistry journals . Some common activity types, such as IC50s, are standardised  to allow broad comparisons across assays; the standardised data can be found in the  standard_value ,  standard_relation  and  standard_units  fields. Original data is retained in the database downloads in the  value ,  relation  and  units  fields. However, we extract all data from a publication including non-numerical bioactivity and ADME data. In these cases, the activity comments may be populated during the ChEMBL extraction-curation process  in order to capture the author's  overall  conclusions . Similarly, for deposited datasets and subsets of other databases (e.g. DrugMatrix, PubChem), th