Skip to main content

Molecule hierarchy




During drug development, active pharmaceutical ingredients are often formulated as salts to provide the final pharmaceutical product. ChEMBL includes parent molecules and their salts (approved and investigational) as well as other alternative forms such as hydrates and radioisotopes. These alternative forms are linked to their parent compound through the molecule hierarchy.
 

Using the molecule hierarchy

The molecule hierarchy can be used to retrieve and display connected compounds and to aggregate activity data that has been mapped to any member of a compound family. On the interface, related compounds are automatically displayed in the ‘Alternative forms’ section of the ChEMBL compound report card. Bioactivity data can easily be aggregated in the activity summary by using the 'Include/Exclude Alternative Forms' filter.



Finding the molecule hierarchy

 

On the interface, we include alternative forms as shown above. The downloaded database contains the molecule_hierarchy table and the equivalent API endpoint is the ‘molecule_form’ (https://www.ebi.ac.uk/chembl/api/data/molecule_form).


Example: using the molecule hierarchy to retrieve drug mechanisms

 

One of the most common questions we’re asked is ‘how to obtain drug mechanisms mapped to any member of a compound family’ and so we've provided a couple of examples below.


From the ChEMBL interface

 

The ‘Drugs’ and ‘Mechanism’ views contain only parent molecules and so mechanisms are automatically mapped to the parent form. However, a general search through the ‘Compounds’ view provides mechanisms mapped to parents and all approved salts which can be found on their respective compound report cards.



From the database with SQL

 

In the downloaded database, the drug mechanism may be mapped to a single member of the compound family in the drug_mechanism table, typically the approved form. For example, the mechanism for atorvastatin is mapped to the calcium salt. However, the molecule hierarchy can be used to link the compound family so that a search using the parent (atorvastatin, CHEMBL1487) returns the mechanism mapped to the approved salt (atorvastatin calcium):

 

select *

from drug_mechanism

where molregno in

    (select molregno

    from molecule_hierarchy

    where (

        parent_molregno in

        (select distinct molregno from molecule_dictionary where chembl_id = 'CHEMBL1487') 

        or molregno in

        (select distinct molregno from molecule_dictionary where chembl_id = 'CHEMBL1487')

               )

     );


Questions? 


Please get in touch on the Helpdesk or have a look through our training materials, recent webinar and FAQs.


Comments

Popular posts from this blog

ChEMBL_27 SARS-CoV-2 release

The COVID-19 pandemic has resulted in an unprecedented effort across the global scientific community. Drug discovery groups are contributing in several ways, including the screening of compounds to identify those with potential anti-SARS-CoV-2 activity. When the compounds being assayed are marketed drugs or compounds in clinical development then this may identify potential repurposing opportunities (though there are many other factors to consider including safety and PK/PD considerations; see for example  https://www.medrxiv.org/content/10.1101/2020.04.16.20068379v1.full.pdf+html ). The results from such compound screening can also help inform and drive our understanding of the complex interplay between virus and host at different stages of infection. Several large-scale drug screening studies have now been described and made available as pre-prints or as peer-reviewed publications. The ChEMBL team has been following these developments with significant interest, and as a contr

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

ChEMBL 26 Released

We are pleased to announce the release of ChEMBL_26 This version of the database, prepared on 10/01/2020 contains: 2,425,876 compound records 1,950,765 compounds (of which 1,940,733 have mol files) 15,996,368 activities 1,221,311 assays 13,377 targets 76,076 documents You can query the ChEMBL 26 data online via the ChEMBL Interface and you can also download the data from the ChEMBL FTP site . Please see ChEMBL_26 release notes for full details of all changes in this release. Changes since the last release: * Deposited Data Sets: CO-ADD antimicrobial screening data: Two new data sets have been included from the Community for Open Access Drug Discovery (CO-ADD). These data sets are screening of the NIH NCI Natural Product Set III in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296183, DOI = 10.6019/CHEMBL4296183) and screening of the NIH NCI Diversity Set V in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296182, DOI = 10.601

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

  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 train a single neural network as a binary multi-label classifier that will output the probability of activity/inactivity for each of the targets (tasks) for a given q

ChEMBL Compound Curation Pipeline

At the end of last year we mentioned that we are now using RDKit for our compound structure processing (see here ). Most excitingly, as a part of this we have been working with Greg Landrum the developer of RDKit over the last year to reimplement our  curation pipeline using RDKit.  The pipeline includes three functions: 1. Check Identifies and validates problem structures before they are added to the database 2. Standardize Standardises chemical structures according to a set of predefined ChEMBL business rules  3. GetParent Generates parent structures of multi-component compounds based on a set of rules and defined list of salts and solvents We are now pleased to announce that we are making all the code from this project freely available in GitHub .  The functions can also now be used through our ChEMBL Beaker   API.  Live notebook with examples available here . For ChEMBL26 (shortly to be released) we have created new molfiles for all the ChEM