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

UniChem 2.0

UniChem new beta interface and web services We are excited to announce that our UniChem beta site will become the default one on the 11th of May. The new system will allow us to better maintain UniChem and to bring new functionality in a more sustainable way. The current interface and web services will still be reachable for a period of time at https://www.ebi.ac.uk/unichem/legacy . In addition to it, the most popular legacy REST endpoints will also remain implemented in the new web services: https://www.ebi.ac.uk/unichem/api/docs#/Legacy Some downtime is expected during the swap.  What's new? UniChem’s current API and web application is implemented with a framework version that’s not maintained and the cost of updating it surpasses the cost of rebuilding it. In order to improve stability, security, and support the implementation and fast delivery of new features, we have decided to revamp our user-facing systems using the latest version of widely used and maintained frameworks, i

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

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

ChEMBL 30 released

  We are pleased to announce the release of ChEMBL 30. This version of the database, prepared on 22/02/2022 contains: 2,786,911 compound records 2,157,379 compounds (of which 2,136,187 have mol files) 19,286,751 activities 1,458,215 assays 14,855 targets 84,092 documents Data can be downloaded from the ChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/ Please see ChEMBL_30 release notes for full details of all changes in this release:  https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_30/chembl_30_release_notes.txt New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document ID CHEMBL4689842):   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 synthesize at least

Multi-task neural network on ChEMBL with PyTorch 1.0 and RDKit

  Update: KNIME protocol with the model available thanks to Greg Landrum. Update: New code to train the model and ONNX exported trained models available in github . The use and application of multi-task neural networks is growing rapidly in cheminformatics and drug discovery. Examples can be found in the following publications: - Deep Learning as an Opportunity in VirtualScreening - Massively Multitask Networks for Drug Discovery - Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set But what is a multi-task neural network? In short, it's a kind of neural network architecture that can optimise multiple classification/regression problems at the same time while taking advantage of their shared description. This blogpost gives a great overview of their architecture. All networks in references above implement the hard parameter sharing approach. So, having a set of activities relating targets and molecules we can tra