Skip to main content

RDKit and Raphael.js



The ChEMBL group had the honour of hosting the second RDKit UGM. It was a great way to catch up with the RDKit community, find out about what they are working and learn about new features the toolkit offers. We gave two talks during the meeting, so if you want to know how Clippy can make interacting with different chemical formats on your desktop easier, go here, and if you want to learn about wrapping RDKit up in a RESTful Web Service a.k.a. Beaker (to be described in future blog post), go here. Many discussions about new features RDKit could offer were had throughout the meeting and one which caught my attention was support for plotting compound images on HTML5 Canvas.

Unable to participate in a hackathon held on the final day, I set about hosting my own small hackathon during the weekend (only 1 attendee). The result of this weekend coding effect was a pull request made against RDKit github repo, introducing the new class called JSONCanvas.

Technical Details

As a general rule of the past, the model for generating image relies on the server to sending a binary representation of the compound (e.g. .png, .jpeg) to the client. With advances in browser technologies, it is now feasible to rely on the client to generate the graphical representation of the compound as it now has access to many methods, which allows it to handle geometrical primitives. It can decide if those primitives should be rendered as SVG, VML or even HTML5 Canvas (check out  Kinetic.js for HTML5 canvas rendering, as it knows how to draw some core shapes on canvas). 

My solution uses Raphael.js - a JavaScript library for drawing vector graphics in the browser. For displaying the graphic is uses SVG on browsers that support this format. On older browsers it will fail over to VML. In the library documentation we can find a very interesting method called Paper.add(). This method accepts JSON containing an array of geometrical objects (such as circle, rectangle, path) to be displayed and returns a handle for manipulating (moving, rotating, scaling) the object as a whole. This means that if we could create a JSON object, which uses shapes to represent a chemical compound, we could draw it or manipulate the compound directly. The new JSONCanvas class produces the previously described JSON object for any* given RDKit compound.

(*I am sure we might find a couple of exceptions)

But why?

1. Cost - reduced server processing required to raster image and often third-party drawing libraries are also required.

2. Bandwidth - reduced bandwidth required to transfer JSON representation of compounds. Also, as it  is text-based you can employ further compression (by configuring your server to send gziped JSON which most modern browsers understand) or using AMF.

3. Accuracy - improved scaling quality made possible with vector graphics.

4. Interactivity - compounds rendered using JSON on the client side can handle standard events such as click, hover, etc. Complex operations (animating, sorting, dragging,...), can also be applied to these objects.

Usage

As an example usage of this technique please look at our chemical game. To give you some idea of scale and performance the game loads 1000 compounds when page first loads. If you want to see raw example please explore source of my demo page. Other examples can involve:

1.  Online compound cloud (similar to tag cloud but with compound images instead of words). Such a cloud can be used to visualise compound similarity.

2. Compound stream - substructure search can sometimes return very large number of results. Such results can be represented as pseudo-infinite stream of compounds - only small portion of results is presented on the screen but scrolling down causes more results to be rendered when older one are discarded.

How can I use it?

1. You can download my fork or RDKit containing all relevant changes.

2. Today Greg Landrum, RDKit creator made his own branch containing modified version of the original pull request, so hopefully this is on it's way to be accepted in master branch in future.

As a group we are happy to participate in such a great open source library!

--
MichaƂ

Comments

Popular posts from this blog

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 vaccines). 71 out of the 882 newly added EMA drugs are only authorised by EMA, rather than from other regulatory bodies e.g.

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

Accessing SureChEMBL data in bulk

It is the peak of the summer (at least in this hemisphere) and many of our readers/users will be on holiday, perhaps on an island enjoying the sea. Luckily, for the rest of us there is still the 'sea' of SureChEMBL data that awaits to be enjoyed and explored for hidden 'treasures' (let me know if I pushed this analogy too far). See here and  here for a reminder of SureChEMBL is and what it does.  This wealth of (big) data can be accessed via the SureChEMBL interface , where users can submit quite sophisticated and granular queries by combining: i) Lucene fields against full-text and bibliographic metadata and ii) advanced structure query features against the annotated compound corpus. Examples of such queries will be the topic of a future post. Once the search results are back, users can browse through and export the chemistry from the patent(s) of interest. In addition to this functionality, we've been receiving user requests for  local (behind the