Skip to main content

New Drug Warnings Browser

As mentioned in the announcement post of ChEMBL 29, a new Drug Warnings Browser has been created. This is an updated version of the entity browsers in ChEMBL (Compounds, Targets, Activities, etc). It contains new features that will be tried out with the Drug Warnings and will be applied to the other entities gradually. The new features of the Drug Warnings Browser are described below.

More visible buttons to link to other entities

This functionality is already available in the old entity browsers, but the button to use it is not easily recognised.

In the new version, the buttons are more visible.



By using those buttons, users can see the related activities, compounds, drugs, mechanisms of action and drug indications to the drug warnings selected. The page will take users to the corresponding entity browser with the items related to the ones selected, or to all the items in the dataset if the user didn’t select any.





Additionally, the process of creating the join query is now handled completely by the backend, this will make it faster and will allow it to handle more items in the future. 

Improved visualization of the download process

When the user triggers a download, the visualization of the process of creating the final file has been improved.


Improved filtering capabilities

Many filtering capabilities have been improved with the Drug Warnings Browser. The histogram filters  will now always show a bar for ‘null’ values, so the user can always know how many items in the dataset have a null value for the property being shown. 


For the text-based properties, users can now search and filter for specific values in the dataset. In the following example, the user can search for terms that start with ‘Cardio’ in the values of the property Warning Class in the dataset:


The matching term can be used to filter out the data.


By clicking on the 3 dots button on the histograms, users can change the presentation of the histogram.




The filters for the number-based properties now allow users to filter by a range. The absolute minimum and maximum values are calculated automatically from the values found in the dataset for that current property.


Custom Filtering

Users can now apply custom filters to the dataset, apart from the ones provided by default. Users can filter the data in more complex ways than the default provided by the predefined filters. To use them, click on the ‘Custom Filtering’ button. 



Clicking on the button will open a panel that indicates whether custom filters are being applied. To edit the custom filters, click on the 'Edit Button’.


The menu that opens has 2 main sections. The section to the left shows the custom filter being applied and allows to edit it. The section to the right provides a query builder that helps users to build queries for the dataset. The custom queries are query strings of Elasticsearch, here you can find more information about the query strings.



To help users understand the structure of the data, there is a dialog that shows the available properties in the dataset.




Also some examples are available for users to apply and see how the custom filters can be created. 





The query builder section helps users to create custom filters by using a graphical interface. If auto-paste is activated, the query will be pasted to the editor at the same time as it is created. 



When a custom filter is being applied, an icon will indicate it when the menu is closed.



Future Plans

The Drug Warnings Browser was created to improve these 'entity browser of pages in ChEMBL. The plan is to replace the other entity browsers with the new version gradually. If you have some feedback or want to report a bug, please contact us: chembl-help@ebi.ac.uk (See also this page for more information)





Comments

Popular posts from this blog

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