Skip to main content

The SureChEMBL map file is out


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 result, we have now produced this so-called map file which connects SureChEMBL compounds and patents.

It is available here.
More information can be found in the README file.

What is this file?

There is a total of 216,892,266 rows in the map, indicating a compound extracted from a specific section of a specific patent document. The format of the file is quite simple: it contains compound information (SCHEMBL ID, SMILES, InChI Key, corpus frequency), patent information (patent ID and publication data), and finally location information, such as the field ID and frequency. The field ID indicates the specific section in the patent where the compound was extracted from (1:Description, 2:Claims, 3:Abstract, 4:Title, 5:Image, 6:MOL attachment). The frequency is the number of times the compound was found in a given section of a given patent. More information on the format of the file in the README file.

How many compounds and patents are there?

There are 187,958,584 unique patent-compound pairs, involving 14,076,090 unique compound IDs extracted from 3,585,233 EP, JP, WO and US patent documents - an average of ~52 compounds per patent. The patent coverage is from 1960 to 31-12-2014 inclusive.

Here's a breakdown of the patents in the map per year and patent authority:




Are these all the compounds and patents in SureChEMBL?

Technically, no - in practice, yes. We excluded chemically annotated patents that are not immediately relevant to life sciences, such as this one. For the filtering, we used a list of relevant IPCR and related patent classification codes. At the same time, we excluded too small, too large, too trivial compounds, along with non-organic and radical/fragment compounds.

Are these compounds genuinely claimed as novel in their respective patents?

Automated methods to assess which are the important and relevant compounds in a pharmaceutical patent is a field of research and one of our future plans. For now, the map file include all extracted chemistry mentioned in all sections of a patent, subject to the filters listed in the previous section. A quick and effective trick to filter out trivial and/or uninformative compounds is to use the corpus frequency column and exclude everything with a value more than, say, 1000. Note that, in this way, you will also exclude drug compounds such as sildenafil, which are casually mentioned in a lot of patents. You could also look for compounds mentioned only in claims, description or images sections by filtering by the corresponding field ID.

What can I do with this?

Well, you can start by 'grepping' for one or more patent IDs or SCHEMBL IDs or InChI keys, followed by further filtering. Many of you will choose to normalise the flat file into 3 database tables (say compounds, documents and doc_to_compound) for centralised access and easy querying.

For example, to find the patents the drug palbociclib has been extracted from:

Any plans to update this map file?  

New patents and chemistry arrive and are stored to SureChEMBL every day. We are planning to release new versions and incremental updates of the map file every quarter, in sync with the update of the compound dump files.

I couldn’t find my compound / patent - this compound should not be there

Don’t forget this an automated, live, high-throughput text-mining effort against an inherently noisy corpus such as patents. We are constantly working on improving data quality. If you find anything strange, let us know.

Can I join more metadata, such as patent assignee and title?

Obviously your first port of call would be the SureChEMBL website for patent metadata, but other services you may wish to use include the EPO web services for programmatic access.

Is there anything else?

Errr, yes. Watch this space for another post on storing and accessing live SureChEMBL data, behind your firewall. 


The SureChEMBL Team

Comments

Popular posts from this blog

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

ChEMBL 29 Released

  We are pleased to announce the release of ChEMBL 29. This version of the database, prepared on 01/07/2021 contains: 2,703,543 compound records 2,105,464 compounds (of which 2,084,724 have mol files) 18,635,916 activities 1,383,553 assays 14,554 targets 81,544 documents Data can be downloaded from the ChEMBL FTP site:   https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_29 .  Please see ChEMBL_29 release notes for full details of all changes in this release: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_29/chembl_29_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document IDs CHEMBL4649982-CHEMBL4649998): 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 synthesiz

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

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

Using autoencoders for molecule generation

Some time ago we found the following paper https://arxiv.org/abs/1610.02415 so we decided to take a look at it and train the described model using ChEMBL. Lucky us, we also found two open source implementations of the model; the original authors one https://github.com/HIPS/molecule-autoencoder and https://github.com/maxhodak/keras-molecules . We decided to rely on the last one as the original author states that it might be easier to have greater success using it. What is the paper about? It describes how molecules can be generated and specifically designed using autoencoders. First of all we are going to give some simple and not very technical introduction for those that are not familiar with autoencoders and then go through a ipython notebook showing few examples of how to use it. Autoencoder introduction Autoencoders are one of the many different and popular unsupervised deep learning algorithms used nowadays for many different fields and purposes. These work wi