Skip to main content

ChEMBL 21 web services update






Traditionally, along with the release of the new ChEMBL version, we have made a few updates to our RESTful API. Below you can find a short description of the most important changes:

 

Data API (https://www.ebi.ac.uk/chembl/api/data/docs):

1. New resources: Since ChEMBL 21 introduced a few new tables, we have made them available via the API. The new resources are:

Moreover, the target_component endpoint has been enhanced to provide a list of related GO terms.

2. Solr-based search: a very popular feature request was the ability to search resources by a keyword. A form of searching was already possible before, using filtering terms, such as [i]contains,[i]startswith and [i]endswtith filters. For example, in order to search molecules for 'metazide' in their preferred name, this filter can be used:


However, this approach has many drawbacks:
  • it's executed on the database level and can be very slow
  • in order to search in several attributes, you have to add the filter separately to each of them, which can result with a very long tail of filters
  • you can't search in one-to-many/many-to-many attributes (for example you cannot search molecule by its synonym because a molecule can have many synonyms)
The good news is that in order to solve this problem, we implemented a solr-based solution using django-haystack. Let's just jump straight into examples:

What if we want to search for some term in molecules, targets and assays at once? No problem, the chembl_id_lookup endpoint can be used for this, for example searching for 'morphine' will look like:


Looking at the results of the last request, it's very easy to tell (by examining the 'entity_type' attribute) that a large number of compounds and assays were returned.

Another important thing to note is that every result of search query has a 'score' attribute, indicating the relevancy of the given result. The results are sorted by the score descending (i.e. the most relevant are always first) and although you can add additional filters, for example:

you cannot change ordering by appending 'order_by=...' attribute.

You may ask, why do we only offer searching for 3 resources (well, 4 including the chembl_id_lookup)? This is because these resources are most popular and most important but we are planning to add more (such as searching in document abstracts, cell descriptions, activities) in the near future. If you have any suggestions about which resources should offer search functionality in the first place, please let us know in comments or write your suggestions to chembl-help@ebi.ac.uk. You can easily check which resources offer searching by looking at our live documentation, where all the searching methods are listed.

Furthermore, we would also appreciate your feedback about the quality of search results. If you believe that some results should have higher relevancy score than others and currently that's not the case, let us know so we can properly adjust boosts.

3. Compound images have transparent background by default. So now you can use them regardless of the color scheme used in your website:





 It's also possible to explicitly specify background color, by appending the 'bgColor=color_name' attribute for example in order to get a nice and warm orange background you have do:



The colour names are the standard names defined for HTML, you can check the full list here.

4. Datatables support: Datatables is one of the most popular jQuery plugins for rendering tabular data. In order for you to use it in a generic way (i.e. write the code in such a way it can use datatables to render data from any API endpoint), we have to be able to provide definitions of columns (e.g. how many columns we have for a given endpoint, are they searchable, sortable, what type of data they contain). This is possible using the schema API method (for example: https://www.ebi.ac.uk/chembl/api/data/molecule/schema.json), that describes every resource in a vary detailed way; however, the data provided by the schema has to be transformed to the format compatible with datatables. This is why we decided to provide another method, which is directly compatible with datatables: https://www.ebi.ac.uk/chembl/api/data/molecule/datatables.json.

Below is an example code snippet that renders a datatable from the target resource. Click on the 'Result' tab to see the table - you can sort by columns, change pages and set the number of rows displayed per page. Notice that if you change the name of the resource in the first line of code (from 'target' to 'source' or 'assay' for example), the columns and data will change as well.



Utils API (https://www.ebi.ac.uk/chembl/api/utils/docs):

There is a small update to the utilities (Beaker) part of the API. There is a new method called ctab2xyz, which converts a molfile to the xyz file format. You will notice the new method is now available in the live docs. Also the compound rendering code has been improved so it's now compatible with the latest versions of Pillow library.

Python client (https://github.com/chembl/chembl_webresource_client):

Our official Python client library has been updated as well in order to reflect recent changes. Just to remind you, in order to get the latest version of the client, you should install it via pip:

pip install -U chembl_webresource_client

Some examples of using recently added resources (drug indications, GO slim, drug metabolism):



Searching is exposed as well, examples below:



Another important change to the client is the integration with UniChem API. The latter deserves a separate blog post, so stay tuned.

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