Protein Ligand Affinity Databases

I came across a nice tabular summary of some existing 'public' 'primary' protein-ligand interaction databases that primarily focus on protein-ligand affinity data (so Ki, Kd, IC50, EC50, etc.), that I have reproduced below (many thanks to Helena Strömbergsson, from Uppsala University for the data).

Name	Target Class Focus	Approx size
BindingDB	All	~48,000
PDSP	Receptors	~47,000
BRENDA	Enzymes	~19,000
BindingMOAD	All	~3,500
PDBBind	All	~3,500
AffinDB	All	~700
PLD	All	~500

The comparable number from StARlite (31) are 507,645 (of which 186,370 are better than 100nM) for affinity class end-points. Oh, and we have started a new load.....

Comments

Unknown said…

This shows how putting this data in the public sector will be a huge boon for scientific researchers! It will also lead many to say "I want that! When can I get it?!" :-)

Keep up the great work!

18 March 2009 at 12:47

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 ...

Improvements in SureChEMBL's chemistry search and adoption of RDKit

Dear SureChEMBL users, If you frequently rely on our "chemistry search" feature, today brings great news! We’ve recently implemented a major update that makes your search experience faster than ever. What's New? Last week, we upgraded our structure search engine by aligning it with the core code base used in ChEMBL . This update allows SureChEMBL to leverage our FPSim2 Python package , returning results in approximately one second. The similarity search relies on 256-bit RDKit -calculated ECFP4 fingerprints, and a single instance requires approximately 1 GB of RAM to run. SureChEMBL’s FPSim2 file is not currently available for download, but we are considering generating it periodicaly and have created it once for you to try in Google Colab ! For substructure searches, we now also use an RDKit -based solution via SubstructLibrary , which returns results several times faster than our previous implementation. Additionally, structure search results are now sorted by...

Improved querying for SureChEMBL

Dear SureChEMBL users, Earlier this year we ran a survey to identify what you, the users, would like to see next in SureChEMBL. Thank you for offering your feedback! This gave us the opportunity to have some interesting discussions both internally and externally. While we can't publicly reveal precisely our plans for the coming months (everything will be delivered at the right time), we can at least say that improving the compound structure extraction quality is a priority. Unfortunately, the change won't happen overnight as reprocessing 167 millions patents takes a while. However, the good news is that the new generation of optical chemical structure recognition shows good performance, even for patent images! We hope we can share our results with you soon. So in the meantime, what are we doing? You may have noticed a few changes on the SureChEMBL main page. No more "Beta" flag since we consider the system to be stable enough (it does not mean that you will never ...

Here's a nice Christmas gift - ChEMBL 35 is out!

Use your well-deserved Christmas holidays to spend time with your loved ones and explore the new release of ChEMBL 35! This fresh release comes with a wealth of new data sets and some new data sources as well. Examples include a total of 14 datasets deposited by by the ASAP ( AI-driven Structure-enabled Antiviral Platform) project, a new NTD data se t by Aberystwyth University on anti-schistosome activity, nine new chemical probe data sets, and seven new data sets for the Chemogenomic library of the EUbOPEN project. We also inlcuded a few new fields that do impr ove the provenance and FAIRness of the data we host in ChEMBL: 1) A CONTACT field has been added to the DOCs table which should contain a contact profile of someone willing to be contacted about details of the dataset (ideally an ORCID ID; up to 3 contacts can be provided). 2) In an effort to provide more detailed information about the source of a deposited dat...

ChEMBL webinar @ School of Chemoinformatics in Latin America

Recently, the ChEMBL team participated in the " School of Chemoinformatics in Latin America " which was kindly organized by José Medina-Franco and Karina Martinez-Mayorga (both at the National Autonomous University of Mexico). The event was very well attended with 1,181 registrants from 79 different countries. 57% of the participants attended from Latin America, 23% from Asia, and around 8% from Africa and Europe, respectively. 52% of the participants were students (undergraduate and graduate students). Distribution by country Distribution by role Participants could learn a bou t the ChEMBL database and UniChem. We covered different topics to answer these questions: • What is ChEMBL and how is it structured ? • Which data does ChEMBL contain ? • How is data extracted from scientic articles ? • How is the data in ChEMBL curated ? • How is drug ...

The ChEMBL-og

Search This Blog