The ChEMBL-og

US-D254080-S SureChEMBL has disrupted the field of patent chemistry by liberating chemical structures and knowledge locked in text and images, and by making the compound-patent associations freely  and fully searchable and accessible on a daily basis to everyone: academics, IP professionals, content providers, software vendors, biotechs, small and big pharma, and related chemical industries . The speed, scale and scope of the data is unprecedented for a public resource.  SureChEMBL has been around for less than two years ; during this time, it has evolved into a full-blown chemistry resource provided by the EMBL-EBI: the SureChEMBL interface was revamped and released last year , including combined keyword and structure-based queries against the annotated patent corpus. All chemistry is integrated with UniChem and there are several ways to access the data in bulk, including flat files and a data client. Very soon, the data will be fully integrated and avai...

It is the peak of the summer (at least in this hemisphere) and many of our readers/users will be on holiday, perhaps on an island enjoying the sea. Luckily, for the rest of us there is still the 'sea' of SureChEMBL data that awaits to be enjoyed and explored for hidden 'treasures' (let me know if I pushed this analogy too far). See here and  here for a reminder of SureChEMBL is and what it does.  This wealth of (big) data can be accessed via the SureChEMBL interface , where users can submit quite sophisticated and granular queries by combining: i) Lucene fields against full-text and bibliographic metadata and ii) advanced structure query features against the annotated compound corpus. Examples of such queries will be the topic of a future post. Once the search results are back, users can browse through and export the chemistry from the patent(s) of interest. In addition to this functionality, we've been receiving user requests for  local (behind the ...

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 cheminformat...

Have you ever wondered which compound is the most popular in ChEMBL? And by popular I don't mean the one which cracks the best jokes at dinner parties; I mean the compound with the largest number of structural analogues or nearest neighbours (NNs). This number also gives an indication of the sparsity or density of the chemical space around a compound and is a useful concept during hit expansion and lead optimisation.  This number of course depends on the fingerprint, the hashing and folding parameters, the similarity coefficient and the threshold. So let's say 2048-bit RDKit Morgan fingerprints with a radius of 2 or 3 (equivalent to ECFP_4 or ECFP_6) and Tanimoto threshold of 0.5. Why so low threshold? For an explanation, see here and here . To calculate this compound 'popularity', one would need to calculate the full similarity matrix of the 1.4M compounds in ChEMBL. This used to be prohibitively computationally expensive just a few years ago; nowadays,...

As many of you know, SureChEMBL taps into the wealth of knowledge hidden in the patent documents. More specifically, SureChEMBL extracts and indexes chemistry from the full-text patent corpus (EPO, WIPO and USPTO; JPO titles and abstracts only) by means of automated text- and image-mining, on a daily basis. We have recently hosted a webinar about it which turned out to be very popular - for those who missed it, the video and slides are here . Besides the interface, SureChEMBL compound data can be accessed in various ways, such as UniChem and PubChem . The full compound dump is also available as a flat file download from our ftp server . Since the release of the SureChEMBL interface last September, we have received numerous requests for a way to access compound and patent data in a batch way. Typical use-cases would include retrieving all compounds for a list of patent IDs, or vice versa , retrieving all patents where one or more compounds have been extracted from. As a...

Happy New Year for 2015 from the ChEMBL group! Release 20 of the ChEMBL database will be happening around the end of the month, and for those who can't wait, here's a preview of the exciting new features you can expect to find there: HELM notation - we have developed an implementation of the Pistoia Alliance's HELM standard for biotherapeutics and will be supplying HELM notation for just under 20K peptides (previously represented by mol files). We will also make our monomer library available in case others wish to use it to generate their own HELM notation. Structural alerts - in place of the old 'Med Chem Friendly' flag used in ChEMBL, we now have an extensive set of structural alerts calculated for the ChEMBL compounds. The data set includes eight different sets of alerts (including sets published by Pfizer, Glaxo, BMS, University of Dundee, NIH MLSMR and PAINS filters) providing more than 1100 distinct SMARTS. Alerts found for a given compound can be vie...

TL;DR: We have just open sourced our Django ORM Model, which describes the ChEMBL relational database schema. This means you no longer need to write another line of SQL code to interact with ChEMBL database. We think it is pretty cool and we are using it in the ChEMBL group to make our lives easier. Read on to find out more.... It is never a good idea to use SQL code directly in python. Let's see some basic examples explaining why: Can you see what is wrong with the code above? SQL keyword `JOIN` was misspelled as 'JION'. But it's hard to find it quickly because most of code highlighters will apply Python syntax rules and ignore contents of strings. In our case the string is very important as it contains SQL statement. The problem above can be easily solved using some simple Python SQL wrapper, such as edendb . This wrapper will provide set of functions to perform database operations for example 'select', 'insert', 'delete': No...

Followers of the ChEMBL group's activities and this blog will be aware of our involvement in the migration of the previously commercially available SureChem chemistry patent system, to a new, free-for-all system, known as SureChEMBL. Today we are very pleased to announce that the migration process is complete and the SureChEMBL website is now online. SureChEMBL provides the research community with the ability to search the patent literature using Lucene-based keyword queries and, much more importantly, chemistry-based queries. If you are not familiar with SureChEMBL, we recommend you review the content of these earlier blogposts here and here . SureChEMBL is a live system, which is continuously extracting chemical entities from the patent literature. The time it takes for a new chemical in the patent literature to become searchable in the SureChEMBL system is 1-2 days (WO patents can sometimes take a bit longer due to an additional reprocessing step). At time of ...

There are now multiple formats and ways to access the ChEMBL data, and we have recently assigned DOIs to all available versions of ChEMBL (and will archive these on the ftp server, permanently). So when you publish use of ChEMBL, could you reference the following papers: ChEMBL Database A. Gaulton, L. Bellis, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, R. Akhtar, A.P. Bento, B. Al-Lazikani, D. Michalovich, & J.P. Overington (2012) ‘ChEMBL: A Large-scale Bioactivity Database For Chemical Biology and Drug Discovery’ Nucleic Acids Res. Database Issue , 40 D1100-1107. DOI:10.1093/nar/gkr777 PMID:21948594 A.P. Bento, A. Gaulton, A. Hersey, L.J. Bellis, J. Chambers, M. Davies, F.A. Krüger, Y. Light, L. Mak, S. McGlinchey, M. Nowotka, G. Papadatos, R. Santos & J.P. Overington (2014) ‘The ChEMBL bioactivity database: an update’ Nucleic Acids Res . Database Issue , 42 1083-1090. DOI:10.1093/nar/gkt103 PMID: 24214965 myChEMBL R. Ochoa, M. Davies...