Skip to main content

Latest activities on the Activities table in ChEMBL_15

For the recent ChEMBL_15 release, a considerable part of our efforts was focussed on the standardisation and harmonisation of the data in the Activities table. The latter holds all the quantitative and qualitative experimental measurements across compounds, assays and targets; needless to say that without it there's no ChEMBL!

This is a summary of what we've incorporated so far:

  1. Flag missing data: Records with null published values and null activity comments were flagged as missing.
  2. Standardise activity types and units: Conversion of heterogenous published activity type descriptions and units to a standard_type and set of standard_units (e.g., for IC50 convert mM/uM/pM measurements to nM).
  3. Flag unusual units: Records with unusual published units for their respective activity types were flagged as 'non standard'. For example, a hypothetical record with IC50 type and units in kg would be flagged!
  4. Convert the log values: The records with activity types such as pKi and logIC50 were appropriately converted to their non-log equivalents (by considering the units and sign of course as well). This updated a whopping 25% of the activities table - this means that significantly more data will become more comparable for subsequent analyses.
  5. Round values: For records with a standard activity value above 10, the rounding was done to the second decimal place. Otherwise, rounding was performed after the first three significant digits. For example 0.00023666666 would become a more concise 0.000237
  6. Check activity ranges: Records with a standard activity value outside the range specified by our expert biological curators, given the standard unit and type, were appropriately flagged.
  7. Detect duplicated values: For this one, we were inspired by a recent publicationWhat we did is we detected and flagged duplicated activity entries and potential transcription errors in activity records that come from publications. The former are records with identical compound, target, activity, type and unit values that were most likely reported as citations of measurements from previous papers, even when these measurements were subsequently rounded. The latter cases consist of otherwise identical entries whose activity values differ by exactly 3 or 6 orders of magnitude indicating a likely error in the units (e.g. uM instead of nM).

As a result of our efforts, we added 2 new columns in the Activities table, namely Data_validity_comment and Potential_duplicate. The former takes one out of 5 possible values: NULL, 'Potential missing data' (see point 1), 'Non standard unit for type' (see point 3), 'Outside typical range' (see point 6) and 'Potential transcription error' (see point 7). The latter column contains a binary (0,1) flag to indicate whether we think the specific activity record is a duplicate, as per point 7 above.

Stay tuned for more posts on the changes/improvements introduced by the new ChEMBL_15 release. Meanwhile, if you have any comments/feedback on the curation process or on the activity types we should prioritise, please let us know



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

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

Natural Product-likeness in ChEMBL

Recently, we included a Natural Product-likeness score for chemical compounds stored in ChEMBL. We made use of an algorithm published by Peter Ertl, Silvio Roggo and Ansgar Schuffenhauer in 2008 .  Whereas the original version of this algorithm used a commercial data set of Natural Product molecules for training the algorithm (the CRC Dictionary of Natural Products) and an in-house library of synthetic molecules as a negative set, we used Greg Landrum's  RDKit implementation  which is based on  ~50,000 natural products collected from various open databases and ~1 million drug-like molecules from ZINC as a "non-Natural Product background". After including the new score into ChEMBL, we were interested to see whether the results look meaningful. We had a handful of simple questions: How is Natural Product-likeness distributed in ChEMBL and how does this compare to Natural Product-likeness for "real" NPs? Can we observe any difference in Natural Product-likeness for

What is Max Phase in ChEMBL?

ChEMBL contains information on drugs that have been approved for treatment of a specific disease / diagnosis (an indication) within a region of the world (e.g. FDA drugs are approved for use in the United States), and clinical candidate drugs that are being investigated for an indication during the clinical trials process.  The maximum phase of development for the compound across all indications is assigned a category called 'max_phase' (the value in brackets is used in the downloadable ChEMBL database in the 'molecule_dictionary' table): Approved (4): A marketed drug e.g. AMINOPHYLLINE ( CHEMBL1370561 ) is an FDA approved drug for treatment of asthma.  Phase 3 (3): A clinical candidate drug in Phase 3 Clinical Trials e.g. TEGOPRAZAN ( CHEMBL4297583 ) is under clinical investigation for treatment of peptic ulcer at Phase 3, and also liver disease at Phase 1.  Phase 2 (2): A clinical candidate drug in Phase 2 Clinical Trials e.g. NEVANIMIBE HYDROCHLORIDE ( CHEMBL542103 )

ChEMBL 32 is released!

  We are pleased to announce the release of ChEMBL 32! This release of ChEMBL comes with a complete update of drug and clinical candidate information, the addition on a Natural Product likeness score and a harmonization of Journal Name abbreviations according to NLM standards. This version of the database, prepared on 26/01/2023 contains: 2,354,965 compounds (of which 2,327,928 have mol files)             2,995,433 compound records (non-unique compounds) 20,038,828 activities 1,536,903 assays 15,139 targets 86,364 documents   Please see ChEMBL 32 release notes for full details of all changes in this release. Data can be downloaded from the ChEMBL FTP site . Please note that on demand Oracle 19c dumps will not be provided anymore after the ChEMBL 34 release. New Deposited Datasets CHEMBL5058649 - Data for DCP probe BAY 1816032 * CHEMBL5058643 - Data for DCP probe BI-2081 * CHEMBL5058646 - Data for DCP probe CCT369260 * CHEMBL5058644 - Data for DCP probe JNJ-39758979 * CHEMBL5058645 - D