ChEMBL Resources

Resources:
ChEMBL
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SureChEMBL
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ChEMBL-NTD
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ChEMBL-Malaria
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The SARfaris: GPCR, Kinase, ADME
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UniChem
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DrugEBIlity
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ECBD

Monday, 1 December 2014

Accessing web services with cURL


ChEMBL web services are really friendly. We provide live online documentation, support for CORS and JSONP techniques to support web developers in creating their own web widgets. For Python developers, we provide dedicated client library as well as examples using the client and well known requests library in a form of ipython notebook. There are also examples for Java and Perl, you can find it here.

But this is nothing for real UNIX/Linux hackers. Real hackers use cURL. And there is a good reason to do so. cURL comes preinstalled on many Linux distributions as well as OSX. It follows Unix philosophy and can be joined with other tools using pipes. Finally, it can be used inside bash scripts which is very useful for automating tasks.

Unfortunately first experiences with cURL can be frustrating. For example, after studying cURL manual pages, one may think that following will return set of compounds in json format:


But the result is quite dissapointing...


The reason is that --data-urlencode (-d) tells our server (by setting Content-Type header) that this request parameters are encoded in "application/x-www-form-urlencoded" - the default Internet media type. In this format, each key-value pair is separated by an '&' character, and each key is separated from its value by an '=' character for example:


This is not the format we used. We provided our data in JSON format, so how do we tell the ChEMBL servers the format we are using? It turns out it is quite simple, we just need to specify a Content-Type header:


If we would like to omit the header, correct invocation would be:


OK, so request parameters can be encoded as key-value pairs (default) or JSON (header required). What about result format? Currently, ChEMBL web services support JSON and XML output formats. How do we choose the format we would like the results to be returned as? This can be done in three ways:

1. Default - if you don't do anything to indicate desired output format, XML will be assumed. So this:


will produce XML.

2. Format extension - you can append format extension (.xml or .json) to explicitly state your desired format:


will produce JSON.

3. `Accept` Header - this header specifies Content-Types that are acceptable for the response, so:


will produce JSON.

Enough boring stuff - Lets write a script!


Scripts can help us to automate repetitive tasks we have to perform. One example of such a task would be retrieving a batch of first 100 compounds (CHEMBL1 to CHEMBL100). This is very easy to code with bash using curl (Note the usage of the -s parameter, which prevents curl from printing out network and progress information):


Executing this script will return information about first 100 compounds in JSON format. But if you carefully inspect the returned output you will find that some compound identifiers don't exist in ChEMBL:


We need to add some error handling, for example checking if HTTP status code returned by server is equal to 200 (OK). Curl comes with --fail (-f) option, which tells it to return non-zero exit code if response is not valid. With this knowledge we can modify our script to add error handling:


OK, but the output still looks like a chaotic soup of strings and brackets, and is not very readable...

Usually we would use a classic trick to pretty print json - piping it through python:


But it won't work in our case:



Why? The reason is that python trick can pretty-print a single JSON document. And what we get as the output is a collection of JSON documents, each of which describes different compound and is written in separate line. Such a format is called Line Delimited JSON and is very useful and well known.

Anyway, we are data scientists after all so we know a plenty of other tools that can help. In this case the most useful is jq - "lightweight and flexible command-line JSON processor", kind of sed for JSON.

With jq it's very easy to pretty print our script output:



Great, so we finally can really see what we have returned from a server. Let's try to extract some data from our JSON collection, let it be chemblId and molecular weight:



Perfect, can we have both properties printed in one line and separated by tab? Yes, we can!



So now we can get the ranking of first 100 compounds sorted by their weight:




Exercises for readers:

1. Can you modify compounds.sh script to accept a range (first argument is start, second argument is length) of compounds?
2. Can you modify the script to read compound identifiers from a file?
3. Can you add a 'structure' parameter, which accepts a SMILES string. When this 'structure' parameter is present, the script will return similar compounds (you can decide on the similarity cut off or add an extra parameter)?



Monday, 10 November 2014

Finding key compounds in med. chemistry patents: The open way


A couple of us attended the 3rd RDKit UGM, hosted by Merck in Darmstadt this year. It was an excellent opportunity to catch up with RDKit developments and applications and meet up with other loyal "RDKitters".

I presented a talk-torial there and went through an IPython Notebook, which some of you may find useful. It uses patent chemistry data extracted from SureChEMBL and after a series of filtering steps, it follows a few "traditional" chemoinformatics approaches with a set of claimed compounds. My ultimate aim was to identify "key compounds" in patents using compound information alone, inspired by papers such as this and this. The crucial difference is that these authors used commercial data and software, where in this implementation everything is free and open. At the same time, I wanted to show off what the combination of pandas, scikit-learn, mpld3, Beaker, RDKit, IPython Notebook and SureChEMBL can do nowadays (hint: a lot). 

So, here is the Notebook and here are the associated slides which give a bit of background and context. 

Obviously, the logic and steps can be reimplemented with other toolkits or workflow tools, such as KNIME


George




Wednesday, 5 November 2014

Using ChEMBL web services via proxy.




It is common practice for organizations and companies to make use of proxy servers to connect to services outside their network. This can cause problems for users of the ChEMBL web services who sit behind a proxy server. So to help those users who have asked, we provide the following quick guide, which demonstrates how to access ChEMBL web services via a proxy.

Most software libraries respect proxy settings from environmental variables. You can set the proxy variable once, normally HTTP_PROXY and then use that variable to set other related proxy environment variables:


Or if you have different proxies responsible for different protocols:


On Windows, this would be:



If you are accessing the ChEMBL web services programmatically and you prefer not to clutter your environment, you can consider adding the proxy settings to your scripts. Here are some python based recipes:


1. Official ChEMBL client library


If you are working in a python based environment, we recommend you to use our client library (chembl_webresource_client), for accessing ChEMBL web services. It already offers many advantages over accessing the ChEMBL web services directly and handling proxies is yet another. All you need to do is configure proxies once and you are done:



2. Python requests library


If you decide to use requests, you have to add 'proxies' parameter to every 'get' and 'post' function call:



3. Python urllib2 library


Finally, in the lowest level library, 'urllib2' you can set a ProxyHandler and register it to URL opener:



We would like to thank Dr. Christine Rudolph for the idea and providing code snippets.

Tuesday, 4 November 2014

An overview and invitation to contribute to ChEMBL curation with PPDMs

PPDMs has been in the making for more than a year and is a follow-up on a conference paper we published in 2012. As in 2012, our objective is to map small molecule binding sites to protein domains, the structural units that form recurring building blocks in the evolution of proteins. An application note describing PPDMs is just out in Bioinformatics.

Mapping small molecule binding to protein domains

The mapping facilitates the functional interpretation of small molecule-protein interactions - if you understand which domain in a protein is targeted, you are in a better position to anticipate the downstream effect.  Mapping small molecule binding to protein domains also provides a technical advantage to machine-learning approaches that incorporate protein sequence information as a descriptor to predict small molecule bioactivity. Reducing the sequence descriptor to the part that mediates small molecule binding increases the informative content of the descriptor. This is best exemplified by the domain-poisoning problem, illustrated below.
Result of a hypothetical query using as input the rat Tyrosine-protein phosphatase Syp (P35235) - and one of the hits, retrieved from a BLAST query against the ChEMBL target dictionary - the rat Tyrosine-protein kinase SYK (Q64725). The significant e-value for this query results from high scoring alignments of the SH2 domains. At the same time, the overlap between small molecules binding both proteins is expected to be low.

A simple heuristic

For individual experiments, it is often quite trivial to decide which domain was targeted. For example, medicinal chemists know whether their compound is a kinase inhibitor or one of a handful of SH2 inhibitors. This knowledge, while easily gleaned by the expert, is implicit and cannot be accessed programmatically. Hence we were motivated to implement a solution that could achieve this across as many measured bioactivities as possible.

Our initial implementation of mapping small molecules to protein domains consisted of a simple heuristic: Identify domains with known small molecule interaction and use these domains as a look-up when mapping measured bioactivities to protein domains. This process is illustrated in the figure below.

A catalogue of validated domains was extracted from assays against single-domain proteins (step 1, 2) and projected onto measured bioactivities in ChEMBL (step 3). Three possible outcomes are: i) A successful mapping if exactly one of the Pfam-A domain models from the catalogue matches the sequence; ii) No mapping if none of the Pfam-A domain models from the catalogue match the sequence; iii) A conflicting mapping if multiple domain models from the catalogue match the sequence.
Despite its simplicity, this method works surprisingly well, owing to the fact that protein domains that are relevant to drug discovery are prioritised in Pfam-A model curation. Another factor that contributes here is the conservative route taken by many drug discovery projects that focus on targets that are in well characterised protein families. However, as illustrated by the cases labelled ii) and iii), some constellations are not covered by the simple heuristic.

A public platform to review and improve mappings


Measured activities in ChEMBL falling into category iii) from the illustration above amount to only a fraction of the total but often reflect interesting biology. DHFR-TS for example is a multi-functional enzyme combining both a DHFR and Thymidylate_synt domain that occurs in the group of bikonts, which includes Trypanosoma and Plasmodium. In humans (and all metazoa), these domains occur as separate enzymes.
Small molecule inhibitors exist for both domains, DHFR (yellow, with Pyrimethamine) and Thymidylate synthase (blue, with Deoxyuridine monophosphate).
We built PPDMs as a platform to resolve such cases. PPDMs aggregates information that supports manual mapping assignments based on medicinal chemistry knowledge. New mappings can be  committed to the PPDMs logs and then transferred to the ChEMBL database in future releases.

The Conflicts section on the website summarises conflicts (cases that correspond to category iii as discussed above) that were encountered when the mapping was applied to measured activities in the ChEMBL database and offers an interface to resolve them.

The Evidence section provides the full catalogue of domains for which we found evidence of small molecule binding. Evidence for the majority of domains in this list is provided in the form of measured bioactivities in ChEMBL, while in a few cases we provide a reference to the literature. These are cases where well-known domains occur exclusively in multi-domain architectures, such as 7tm_2 and 7tm_3. The catalogue can be downloaded in full from this section.

PPDMs also provides logs of individual assignments - these can be queried by date, user and comments left when the assignment was made. A log of all assigned mappings can be downloaded from this section. Another way to review assigned mappings is through the Resolved section, where assignments are grouped by domain architecture.

We invite everyone with an interest in the matter to sign up with PPDMs, whether it's simply for playing around, resolving remaining conflicts, or reviewing existing assignments.  Please get in touch and we'll sort out a login for you!

felix

Tuesday, 28 October 2014

Paper: PPDMs – A resource for mapping small molecule bioactivities from ChEMBL to Pfam-A protein domains


We've just published a Open Access paper in Bioinformatics on an approach to annotate the region of ligand binding within a target protein. This has a lot of applications in the use of ChEMBL, in particular providing greater accuracy in mapping functional effects, improving ligand-based target prediction approaches, and reducing false positives in sequence/target searching of ChEMBL. Where next for this work - well annotating to a site-specific level would be a good thing to implement (think about HIV-1 RT with the distinct nucleoside and non-nucleoside sites).

Here's the abstract...

Summary: PPDMs is a resource that maps small molecule bioactivities to protein domains from the Pfam-A collection of protein families. Small molecule bioactivities mapped to protein domains add important precision to approaches that use protein sequence searches alignments to assist applications in computational drug discovery and systems and chemical biology. We have previously proposed a mapping heuristic for a subset of bioactivities stored in ChEMBL with the Pfam-A domain most likely to mediate small molecule binding. We have since refined this mapping using a manual procedure. Here, we present a resource that provides up-to-date mappings and the possibility to review assigned mappings as well as to participate in their assignment and curation. We also describe how mappings provided through the PPDMs resource are made accessible through the main schema of the ChEMBL database.

Availability: The PPDMs resource and curation interface is available at https://www.ebi.ac.uk/chembl/research/ppdms/pfam_maps

The source-code for PPDMs is available under the Apache license at https://github.com/chembl/pfam_maps

Source code is available at https://github.com/chembl/pfam_map_loader to demonstrate the integration process with the main schema of ChEMBL.

Monday, 27 October 2014

Django model describing ChEMBL database.





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':


Now it's harder to make a typo in any of SQL keywords because they are exposed to python so IDE should warn you about mistake.

OK, time for something harder, can you find what's wrong here, assuming that this query is executed against chembl_19 schema:


Well, there are two errors: first of all `molecule_synonyms` table does not have a `synonims` column. The proper name is `synonyms`. Secondly, there is table name typo  `molecule_synonyms`.

This kind of error is even harder to find because we are dealing with python and SQL code that is syntactically correct. The problem is semantic and in order to find it we need to have a good understanding of the underlying data model, in this case the chembl_19 schema. But the ChEMBL database schema is fairly complicated (341 columns spread over 52 tables), are we really supposed to know it all by heart? Let's leave this rhetorical question and proceed to third example: how to query for compounds containing the substructure represented by 'O=C(Oc1ccccc1C(=O)O)C' SMILES:

For Oracle this would be:


And for Postgres:


As you can see both queries are different, reasons for these differences are:
  1. Differences in Oracle and Postgres dialects
  2. Different chemical cartridges (Accelrys Direct and RDKit)
  3. Different names of auxiliary tables containing binary molecule objects
These queries are also more complicated than the previous examples as they require more table joins and they make calls to the chemical cartridge-specific functions.

The example substructure search queries described above are similar to those used by the ChEMBL web services, which are available on EBI servers (Oracle backend) and in the myChEMBL VM (PostgreSQL backend). Still, the web services work without any change to their code. How?

All of the problems highlighted in this blogpost can be solved by the use of a technique known as Object Relational Mapping (ORM). ORM converts every table from database (for example 'molecule_dictionary') into Python class (MoleculeDictionary). Now it's easy to create a list of all available classes in Python module (by using 'dir' function) and check all available fields in class which corresponds to columns from SQL tables. This makes database programming easier and less error prone. The ORM also allows the code to work in a database agnostic manner and explains how we use the same codebase with Oracle and PostgreSQL backends.

If this blogpost has convinced you to give the ORM approach a try, please take a look at our ChEMBL example also included in myChEMBL:

Wednesday, 22 October 2014

myChEMBL 19 Released



                     
We are very pleased to announce that the latest myChEMBL release, based on the ChEMBL 19 database,  is now available to download. In addition to the extra data, you will also find a number a great new features. So what's new then?

More core chemoinformatics tools

We have included OSRA (Optical Structure Recognition), which is useful for extracting compound structures from images. OSRA can be accessed from the command line or by very convenient web interface, provided by Beaker (described below). We've also added OpenBabel - another great open source cheminformatics toolkit. This means you can now experiment with both RDKit and OpenBabel and use whichever you prefer.

ChEMBL Beaker

myChEMBL now ships with a local instance the ChEMBL Beaker service. For those not familiar with Beaker, the service provides users with an array of chemoinformatics utilities via a RESTful API. Under the hood, Beaker is using RDKit and OSRA to carry out its methods. With the addition of Beaker in myChEMBL, users can now carry out the following tasks in secure local environment:
  • Convert chemical structure bewteen multiple formats
  • Extract compound information from images and pdfs
  • Generate compound images in raster (png) and vector (svg) forms
  • Generate HTML5 ready representation of compound structure
  • Generate compound fingerprints
  • Generate compound descriptors
  • Identify Maximum Common Substructure
  • Compound standardisation
  • Lots of more calculations

 

New IPython notebooks

We have written a number of new IPyhthon notebooks, which focus on a range of cheminformatics and bioinformatic topics. The topics covered by the new notebooks include:
  • Introduction on how to use ChEMBL Beaker
  • Using the Django ORM to query the ChEMBL database
  • Introduction to BLAST and creation of a simple Druggability Score
  • Introduction to machine learning
  • Analysis of SureChEMBL data, focused on identifying the MCS core identified in a patent 
  • Extraction and analysis of ChEMBL ADME data 

We have also updated the underlying Ubuntu VM to 14.04 LTS, which also required us to make a number of changes the myChEMBL installation. To see how these changes and new additions have effected a bare metal installation of myChEMBL, head over the myChEMBL github repository.

 

Installation

There are 2 different ways we recommend for installing myChEMBL:
  1. Follow the instructions in the INSTALL file on the ftpsite. This will import the myChEMBL VM into VirtualBox
  2. Use Vagrant to install myChEMBL. See this earlier blogpost for more details, but the command to run is:
vagrant init chembl/myChEMBL && vagrant up

   If you already have myChEMBL_18 installed via Vagrant, instead of running 'vagrant box update', we strongly recommend running: 

vagrant box remove chembl/myChEMBL
vagrant init chembl/myChEMBL && vagrant up

Future plans

The myChEMBL resource is an evolving system and we are always looking to add new open source projects, tools and notebooks. We would be really interested to hear from users about what they would like to see in future myChEMBL releases, so please get in touch if you have any suggestions. (Just so you know, we already have a couple of ideas for myChEMBL 20).

We hope you find this myChEMBL update useful and if you spot any issues or have any questions let us know.

The myChEMBL Team

Thursday, 16 October 2014

New Drug Approvals 2014 - Pt. XII - Naloxegol (Movantik™)




ATC Code: A06AH03
Wikipedia: Naloxegol
ChEMBL: CHEMBL2219418

On September 16th FDA approved Movantik (naloxegol, AZ-13337019), as an oral treatment for patients with opioid-induced constipation and chronic non-cancer pain.

Naloxegol
Naloxegol is an opioid receptor antagonistDue to its similarity to noroxymorphone, a main metabolite of oxycodone, naloxegol is classed as a controlled substance. However, the FDA analysed its abuse potential and concluded that there was no risk of dependency.





Mode of Action
Opioids are a class of drugs which are used to manage pain, but have a common side effect of reducing the motility of the gastrointestinal tract, making bowel movements difficult. Opioids work by binding to the mu-receptors (CHEMBL233, UniProt:P35372) in the central nervous system, thereby reducing pain. However, they are also able to bind to the mu-receptors in the gastrointestinal tract, hence causing opioid-induced constipation. 
Movantik is a peripherally-acting opioid receptor antagonist, which is able to prevent constipation by reducing this specific side effect of the opioids without affecting the efficacy of the pain management.

Clinical Trials
The clinical trials for this drug were carried out on a KODIAC clinical programme, comprising of four studies. Tests showed that 44% and 41% of patients receiving 25mg and 12.5mg, respectively, experienced increased bowel movements, compared to just 29% who took the placebo. [Paper]


Indication and Warnings
This drug is for non-cancer related pain. Side effects have been abdominal pain, diarrhea, headache and excessive gas in the stomach and/or intestinal area. 
When used in conjunction with another peripherally-acting opioid antagonist, there is the chance of gastrointestinal perforation.
There is also the chance of withdrawal symptoms.
This is contraindicated for anyone who is also taking CYP3A4 (CHEMBL5792, UniProt:Q9HB55) inhibitors, such as clarithromycin (CHEMBL1741), as this will increase the exposure to naloxegol and could precipitate opioid withdrawal symptoms. [FDA]

Trade Names
Naloxegol was developed by AstraZeneca and is marketed under the trade name of Movantik. It is due for release during the first quarter of 2015.

New Drug Approvals 2014 - Pt. XI - Idelalisib (Zydelig™)




ATC Code: L01XX47
Wikipedia: Idelalisib
ChEMBL: CHEMBL2216870

On July 23rd the FDA approved Zydelig (idelalisib, GS-1101), as an orally-delivered drug to treat patients with three types of blood cancers.
Relapsed chronic lymphocytic leukemia (CLL)
Relapsed follicular B-cell, non-Hodgkin lymphoma  (FL)
Relapsed small lymphocytic lymphoma (SLL)

Blood cancer
The three main categories of blood cancer are leukemia, lymphoma and myeloma. Lymphoma is also split into two types: Hodgkin lymphoma and non-Hodgkin lymphoma. Both leukemia and myeloma occur in the bone marrow, whilst lymphoma is a cancer that is isolated to the lymphatic system. Acute leukemia is where there is an abundance of underdeveloped white blood cells that can’t function properly and chronic leukemia is where there are just far too many white blood cells, which is just as bad as having too few. Myeloma is where the plasma cells form tumours in the bone marrow.


Idelalisib
This drug is a phosphoinositide 3-kinase inhibitor, which works by blocking P110σ (CHEMBL3130, Uniprot:O00329), the delta isoform of the phosphoinositide 3-kinase enzyme, encoded in humans by the PIK3CD gene. This isoform plays a role in B-cell development, proliferation and function and is expressed predominantly in leukocytes.

Mode of action
Idelalisib works on patients by inhibiting the PI3 kinase delta isoform (PI3Kδ), which plays an important role in malignant lymphocyte survival. It is the delta and gamma forms that are specific to the hematopoietic system. This treatment impairs the normal tracking of CLL lymph nodes. It can be used in conjunction with Rituxan (rituximab), an existing blood cancer treatment, for relapsed CLL and on its own for FL and SLL.

Clinical trials
Clinical trials were carried out on 220 patients, with relapsed CLL, who were not healthy enough, due to co-existing medical conditions or damage from previous chemotherapy, to receive cytotoxic therapy. Patients were administered either idelalisib plus rituximab or a placebo and rituximab. Most of these patients were 65 years of age or older.
After 24 weeks, 93% of the group who had taken the combination treatment were disease progression-free, compared to only 46% of the group who had received the placebo and rituximab combination.
After 12 months, 90% of the dual drug combination group were alive, compared to 80% of the placebo-containing group. [NCI]

Indication and Warnings
This drug can be used in combination with rituximab or on its own, indicated for patients with relapsed conditions. There are several warnings for idelalisib, including hepatotoxicity, pneumonitis (fatal and serious), intestinal perforation and embyro-fetal toxicity. [FDA]

Trade Names
Idelalisib was developed by Gildead Sciences and is marketed under the name Zydelig.

Monday, 29 September 2014

The great US patent spike on SureChEMBL


Apparently, there was a huge spike of new granted US patents released by the USPTO a few days ago. The reason?

In March 2013, US patent law changed. The ‘first to invent’ became ‘first inventor to file’ for patent protection purposes (see more on this here). As a result, a lot of people rushed to submit applications just before the change. Fast forward 18 months later (last week), a huge spike in USPTO granted patents is observed. 

Did SureChEMBL pick that up? See below the cumulative count plot of new patent documents:

And the corresponding compound count extracted from these patents:

For more information on SureChEMBL, see our previous posts.

George

Friday, 19 September 2014

SureChEMBL Available Now





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 writing this blogpost the number of unique compounds in SureChEMBL is 15,760,514, which have been extracted from 12,949,021 patents.

To get started using SureChEMBL, head over to the homepage, where you will be presented with a range of search methods and filters. The image below provides a brief overview of the search functionality offered by the system:




To provide an example of how to use the SureChEMBL website, let's assume you are interested in patents which contained structures similar (or identical) to Sildenafil in the claims section of the document and also mention the term PDE5 anywhere in the document. To run this search, go to the SureChEMBL homepage and carry out the following actions:
  1. Enter the term 'PDE5' in the search text box 
  2. Sketch in the structure of Sildenafil (or use the name look-up function)
  3. Change the search type to similarity (>85%) 
  4. Click the 'Claims' checkbox in the document filter section and 
  5. Hit 'Search' button


After clicking 'Search', you will be presented with a page which contains all compounds that match your search criteria:





From the compound results page above you then have the choice of either exporting the chemistry (all the compounds returned by the search) or viewing the patents associated with 1 more of the selected compounds. For the selected compounds in this search, the associated patents (sorted by descending publication date) are :


 

From the patent document results page, you are able to export chemistry from all documents on display, view patent family information and view the chemistry-annotated, full text document. The claims section of the first patent (US-20140255433-A1) includes references to both sildenafil and PDE5:


 

The aim of this blogpost is to introduce the SureChEMBL system and not to provide a comprehensive review of all the functionality the system offers. This will be covered in future training sessions and webinars, which will be announced on this blog in the near future.

We would like to thank the people over at Digital Science, who were responsible for building the original SureChem system and supported its migration over to EMBL-EBI. In particular, we would like to thank Nicko Goncharoff, James Siddle and Richard Koks.

The system runs on the cloud - specifically on Amazon Web Services, a stable, secure and highly scalable way to deploy web applications. We need to keep a close eye on performance and patterns of usage over the coming weeks, to get an idea of how many servers, etc, we need for full deployment. In particular, we will throttle scripted access,  so please get in touch if you want to try anything like this, so you are not frustrated by slow performance, and we will try and accommodate your use case. There is also a download link on the homepage, so please explore this if you are interested.

We have an exciting roadmap for the future development of SureChEMBL, bt if you have any priority requests, mail them to surechembl-help (at) ebi.ac.uk.

If you experience any issues with the system, or have any questions please get in touch.

Tuesday, 9 September 2014

Papers: Literature text mining and extensions to UniChem


Two new papers from the group have just been published, both in Journal of Chemoinformatics - and of course both Open Access.

The first deals with some extensions to UniChem to allow far more flexible searches. The abstract is:

UniChem is a low-maintenance, fast and freely available compound identifier mapping service, recently made available on the Internet. Until now, the criterion of molecular equivalence within UniChem has been on the basis of complete identity between Standard InChIs. However, a limitation of this approach is that stereoisomers, isotopes and salts of otherwise identical molecules are not considered as related. Here, we describe how we have exploited the layered structural representation of the Standard InChI to create new functionality within UniChem that integrates these related molecular forms. The service, called ‘Connectivity Search’ allows molecules to be first matched on the basis of complete identity between the connectivity layer of their corresponding Standard InChIs, and the remaining layers then compared to highlight stereochemical and isotopic differences. Parsing of Standard InChI sub-layers permits mixtures and salts to also be included in this integration process. Implementation of these enhancements required simple modifications to the schema, loader and web application, but none of which have changed the original UniChem functionality or services. The scope of queries may be varied using a variety of easily configurable options, and the output is annotated to assist the user to filter, sort and understand the difference between query and retrieved structures. A RESTful web service output may be easily processed programmatically to allow developers to present the data in whatever form they believe their users will require, or to define their own level of molecular equivalence for their resource, albeit within the constraint of identical connectivity.

The second deals with using text mining approaches to find papers that look like they could be abstracted into ChEMBL - that is they contain keywords enriched in medicinal chemistry and compound structure concepts. The abstract for this paper is:


The large increase in the number of scientific publications has fuelled a need for semi- and fully automated text mining approaches in order to assist in the triage process, both for individual scientists and also for larger-scale data extraction and curation into public databases. Here, we introduce a document classifier, which is able to successfully distinguish between publications that are ‘ChEMBL-like’ (i.e. related to small molecule drug discovery and likely to contain quantitative bioactivity data) and those that are not. The unprecedented size of the medicinal chemistry literature collection, coupled with the advantage of manual curation and mapping to chemistry and biology make the ChEMBL corpus a unique resource for text mining.
The method has been implemented as a data protocol/workflow for both Pipeline Pilot (version 8.5) and KNIME (version 2.9) respectively. Both workflows and models are freely available at: ftp://ftp.ebi.ac.uk/pub/databases/chembl/text-mining. These can be readily modified to include additional keyword constraints to further focus searches.
Large-scale machine learning document classification was shown to be very robust and flexible for this particular application, as illustrated in four distinct text-mining-based use cases. The models are readily available on two data workflow platforms, which we believe will allow the majority of the scientific community to apply them to their own data.

%T UniChem: extension of InChI-based compound mapping to salt, connectivity and stereochemistry layers
%A J Chambers
%A M Davies
%A A Gaulton
%A G Papadatos
%A A Hersey
%A JP Overington
%J Journal of Cheminformatics 
%D 2014
%V 6:43  
%O doi:10.1186/s13321-014-0043-5
%O http://www.jcheminf.com/content/6/1/43

%T A document classifier for medicinal chemistry publications trained on the ChEMBL corpus
%A G Papadatos
%A GJP van Westen
%A S Croset
%A R Santos
%A S Trubian
%A JP Overington
%J Journal of Cheminformatics 
%D 2014
%V 6:40  
%O doi:10.1186/s13321-014-0040-8
%O http://www.jcheminf.com/content/6/1/40

Tuesday, 2 September 2014

We're hiring! Web developer for NIH Illuminating the Druggable Genome (IDG) project


We got a prize today, so we are happy. What better way to celebrate, than to recruit someone new for the group. We have a position available for a developer to support web service development and integration for the Knowledge Management Centre part of the recently announced NIH Illuminating the Druggable Genome project, see this link for details of the job.

Closing deadline for applications is 12th October 2014.

Thursday, 28 August 2014

SureChEMBL Update 1



As announced in the previous SureChEMBL blogpost, the temporary holding page is now in place. So when users visit https://www.surechem.com (or https://open.surechem.com), you will be redirected to https://www.surechembl.org.

For updates on the release of the new SureChEMBL site, please keep an eye on the ChEMBL-og.

Tuesday, 26 August 2014

SureChEMBL Coming Very Soon


In the coming weeks we will be very pleased to announce the release of the new SureChEMBL website. Since the beginning of the year, we have been working hard with the folks over at Digital Science, along with all the content and software providers to get the system setup and running on our own Amazon Web Service controlled environment. As we approach the final stages of the transition, we will need to temporarily halt access to the original SureChem site. The reason for this minor disruption is to allow us to complete the testing of the additional functionality we have added to the SureChEMBL user interface.


We will use ChEMBL-og as the primary route of communicating with users, so if you want to be kept up to date, bookmark the site. We will also make ad hoc tweets about SureChEMBL on @johnpoverington, @georgeisyourman, @surechembl and @chembl.


SureChEMBL User Interface


Users familiar with the previous SureChem UI will find a lot in common with the new SureChEMBL UI. A summary of the changes and new features we have added to the SureChEMBL UI are provided below:
  • A user account is no longer required to access the system
  • All users will have access to ‘Pro’ account features, which include chemistry exports, PDF downloads and enhanced search filters
  • UniChem has been integrated and provides dynamic cross references to external chemical resources
  • The new SCHEMBL identifier is used throughout the interface.
  • Updated compound sketchers (Latest Marvin JS and JSME)
  • Rebranding of headers and footers and removing old SureChem references
We will be keeping an eye on usage of the UI, and don;t know what to expect in terms of new users. We will then review scaling hardware to cope with the load now that the default 'Pro' system is open to all.


SCHEMBL Identifier


In line with ChEMBL IDs, all compounds in SureChEMBL have been given SCHEMBL identifiers. For example, SCHEMBL1353 corresponds to 2-(acetyloxy)benzoic acid, aka aspirin. The identifier can be used to access the SureChEMBL compound page and will be included in all SureChEMBL downloads.


SureChEMBL Data Content


The SureChEMBL pipeline has been running daily throughout the summer and has now processed and extracted an additional ~400,000 novel compounds from patents since SureChem’s pipeline freeze. At the time of writing (16:22 22/08/14), the SureChEMBL counts are:
  • Total number of compounds 15,668,22
  • Total number of annotated patents 12,888,125
The rate of novel compounds and annotated patents is truly staggering: There are approximately 80,000 compounds extracted from 50,000 patents that are added to the system every month. Moreover, the latency for a new patent document from its application date to becoming searchable in the system is only between 2 and 7 days, in most cases.


SureChEMBL and UniChem


The complete SureChEMBL structure repository has been added to UniChem (src_id=15)  and consists of 15.2M unique structures mapped to their SCHEMBL IDs. SureChEMBL updates will be added to UniChem on a weekly basis, so that UniChem will be up to date with novel patent chemistry.


SureChEMBL Data Access


Besides availability in UniChem, the complete SureChEMBL structure repository is provided as SD and tsv file in our ftp site:


It has to be emphasised here that this is the raw compound feed as extracted automatically from text and images and is provided without any further filtering or manual curation. This feed contains fragments, radicals, atoms with wrong valencies, polymers and other oddities but if you are the sort of person who wants to use this raw data, you will know what and how to filter things you don't like out.

The chemical registry rules between SureChEMBL and ChEMBL have not been fully aligned yet - they use fundamentally different toolkits - so there are sometimes multiple SCHEMBL ids for the same InChI - if you know this is an issue, you will know how to fix it for your local purposes if you download the data.

Initially, the SureChEMBL files on the ftp site will be updated on a quarterly basis.


SureChEMBL Future Plans

 

Going forward we have many plans related to SureChEMBL, some of which are linked to our involvement in the Open PHACTS project. Our current plans include:
  • Extraction of biological entities from the patent literature
  • SureChEMBL API release 
  • Updated workflow tool integration (e.g. KNIME and Pipeline Pilot)
You will hear more about these plans over the coming year, but our top priority now is to deliver the new SureChEMBL user interface.


If you have any questions about the new SureChEMBL system and data please get in touch

Saturday, 16 August 2014

Citing ChEMBL, and Data DOIs


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, G. Papadatos, F. Atkinson and J.P. Overington (2014) 'myChEMBL: A virtual machine implementation of open data and cheminformatics tools' Bioinformatics. 30 298-300. DOI10.1093/bioinformatics/btt666 PMID: 24262214

ChEMBL RDF
S. Jupp, J. Malone, J. Bolleman, M. Brandizi, M. Davies, L. Garcia, A. Gaulton, S. Gehant, C. Laibe, N. Redaschi, S.M Wimalaratne, M. Martin, N. Le Novère, H. Parkinson, E. Birney and A.M Jenkinson (2014) 'The EBI RDF Platform: Linked Open Data for the Life Sciences' Bioinformatics 30 1338-1339 DOI:10.1093/bioinformatics/btt765 PMID:24413672

Also please reference the version of ChEMBL you may have used in any published analyses, using the following DOIs:

Dataset
DOI
ChEMBL

CHEMBL01
10.6019/CHEMBL.database.01
CHEMBL02
10.6019/CHEMBL.database.02
CHEMBL03
10.6019/CHEMBL.database.03
CHEMBL04
10.6019/CHEMBL.database.04
CHEMBL05
10.6019/CHEMBL.database.05
CHEMBL06
10.6019/CHEMBL.database.06
CHEMBL07
10.6019/CHEMBL.database.07
CHEMBL08
10.6019/CHEMBL.database.08
CHEMBL09
10.6019/CHEMBL.database.09
CHEMBL10
10.6019/CHEMBL.database.10
CHEMBL11
10.6019/CHEMBL.database.11
CHEMBL12
10.6019/CHEMBL.database.12
CHEMBL13
10.6019/CHEMBL.database.13
CHEMBL14
10.6019/CHEMBL.database.14
CHEMBL15
10.6019/CHEMBL.database.15
CHEMBL16
10.6019/CHEMBL.database.16
CHEMBL17
10.6019/CHEMBL.database.17
CHEMBL18
10.6019/CHEMBL.database.18
CHEMBL19
10.6019/CHEMBL.database.19


ChEMBL-RDF

ChEMBL-RDF/16.0
10.6019/CHEMBL.RDF.16.0
ChEMBL-RDF/17.0
10.6019/CHEMBL.RDF.17.0
ChEMBL-RDF/18.0
10.6019/CHEMBL.RDF.18.0
ChEMBL-RDF/18.1
10.6019/CHEMBL.RDF.19.0


myChEMBL

myChEMBL-17_0
10.6019/CHEMBL.myCHEMBL.17.0
myChEMBL-18_0
10.6019/CHEMBL.myCHEMBL.18.0

Future releases will adhere to the following patterns. We will be modifying the attribution part of the ChEMBL license to require reporting of these DOIs in publications that use ChEMBL. We hope this will contribute to reproducibility of analyses.