Skip to main content

Data checks

 


ChEMBL contains a broad range of binding, functional and ADMET type assays in formats ranging from in vitro single protein assays to anti proliferative cell-based assays. Some variation is expected, even for very similar assays, since these are often performed by different groups and institutes. ChEMBL includes references for all bioactivity values so that full assay details can be reviewed if needed, however there are a number of other data checks that can be used to identify potentially problematic results.

1) Data validity comments:

The data validity column was first included in ChEMBL v15 and flags activities with potential validity issues such as a non-standard unit for type or activities outside of the expected range. Users can review flagged activities and decide how these should be handled. The data validity column can be viewed on the interface (click 'Show/Hide columns' and select 'data validity comments') and can be found in the activities table in the full database.

* Acceptable ranges/units for standard_types are provided in the ACTIVITY_STDS_LOOKUP table. An exception is made for certain fragment-based activities (MW <= 350) where the data validity comment is not applied.

2) Confidence scores:

The confidence scores reflect both the target type and the confidence that the mapped target is correct (e.g. score 0 = no target assigned, score 9 = direct single protein target assigned). In cases where target protein accessions were unavailable during initial mapping, homologues from different species/strains have sometimes been assigned with lower confidence scores. Curation is ongoing and confidence scores may change between releases as additional assays are mapped (or re-mapped) to targets. The confidence scores can be viewed on the interface and are found in the assays table in the database.


3) Activity comments:

Activity comments capture the author or depositor’s overall activity conclusions and may take into account counter screens, curve fitting etc. It may be worth reviewing the activity comments to identify cases where apparently potent compounds have been deemed inactive by depositors. For further details on activity comments, see our previous Blog post. The activity comments can be viewed on the interface and are available in the activities table of the database.

4) Potential duplicates:

Bioactivity data is extracted from seven core journals and this may include secondary citations. Potential duplicates are flagged when identical compound, target, activity, type and unit values are reported. The potential duplicates field is available on the interface and is found in the activities table of the database.

5) Variants:

Protein variation can change the affinity of drugs for targets. On the interface, variant proteins are recorded in the assay descriptions which can be used to check whether activities correspond to variant or 'wild-type' targets. The variant sequences table was added to the database in version 22 and is linked to the assays table through the variant ID. The variant ID can be used to include or exclude variants from assay results. Curation is underway to annotate additional variants from historical assays (more on this to follow).

Hopefully this provides an idea of some of the available data checks. Questions? Please get in touch on the Helpdesk or have a look through our training materials and FAQs.

Data checks using Imatinib as an example:










Comments

Popular posts from this blog

UniChem 2.0

UniChem new beta interface and web services We are excited to announce that our UniChem beta site will become the default one on the 11th of May. The new system will allow us to better maintain UniChem and to bring new functionality in a more sustainable way. The current interface and web services will still be reachable for a period of time at https://www.ebi.ac.uk/unichem/legacy . In addition to it, the most popular legacy REST endpoints will also remain implemented in the new web services: https://www.ebi.ac.uk/unichem/api/docs#/Legacy Some downtime is expected during the swap.  What's new? UniChem’s current API and web application is implemented with a framework version that’s not maintained and the cost of updating it surpasses the cost of rebuilding it. In order to improve stability, security, and support the implementation and fast delivery of new features, we have decided to revamp our user-facing systems using the latest version of widely used and maintained frameworks, i

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

ChEMBL 30 released

  We are pleased to announce the release of ChEMBL 30. This version of the database, prepared on 22/02/2022 contains: 2,786,911 compound records 2,157,379 compounds (of which 2,136,187 have mol files) 19,286,751 activities 1,458,215 assays 14,855 targets 84,092 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/ Please see ChEMBL_30 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/chembl_30_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document ID CHEMBL4689842):   The EUbOPEN consortium is an Innovative Medicines Initiative (IMI) funded project to enable and unlock biology in the open. The aims of the project are to assemble an open access chemogenomic library comprising about 5,000 well annotated compounds covering roughly 1,000 different proteins, to synthesize at least

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