Skip to main content

Identifying relevant compounds in patents

 


As you may know, patents can be inherently noisy documents which can make it challenging to extract drug discovery information from them, such as the key targets or compounds being claimed. There are many reasons for this, ranging from deliberate obfuscation through to the long and detailed nature of the documents. For example, a typical small molecule patent may contain extensive background information relating to the target biology and disease area, chemical synthesis information, biological assay protocols and pharmacological measurements (which may refer to endogenous substances, existing therapies, reaction intermediates, reagents and reference compounds), in addition to description of the claimed compounds themselves. 

The SureChEMBL system extracts this chemical information from patent documents through recognition of chemical names, conversion of images and extraction of attached files, and allows patents to be searched for chemical structures of interest. However, the current SureChEMBL data extraction pipeline cannot distinguish between the different types of chemicals described in a patent document - it simply extracts all identified molecules. Some simple metrics can be used to filter out the worst offenders. For example, the corpus frequency can be used to remove molecules that are seen thousands of times over many patents (not novel) and chemical descriptors can be used to filter molecules that are particularly small (e.g., fragments, ions, solvents) or don't have drug-like properties. However, there is still much room for improvement and a need for additional methods to more accurately identify claimed compounds.

This paper on the 'Identification of the Core Chemical Structure in SureChEMBL Patents' has recently been published by Maria Falaguera and Jordi Mestres, and the resulting data set is available to download from the SureChEMBL FTP site: https://ftp.ebi.ac.uk/pub/databases/chembl/SureChEMBLccs. The paper describes a filtering protocol to automatically select the core chemical structures best describing the pharmacologically relevant molecules in a patent. The method is based on identifying maximum common substructures (MCSs) for all compounds in a patent, using RDKit. These are filtered to remove those that are particularly promiscuous, then candidate MCSs are chosen according to coverage, homogeneity and inclusion criteria, to identify those that are most likely to represent the core chemical structure of the patent claim. These candidate MCSs are then used to retain only molecules from the patent that contain at least one such substructure (or those with high similarity to a molecule that does). 

The method has been validated against a set of patents containing pharmacology data that have been manually extracted for inclusion in ChEMBL. Since the compounds included in ChEMBL all have reported activity measurements in the patents, it is reasonable to assume these are highly relevant molecules. The filtering method was able to recover 92.5% of these molecules from the corresponding patents (see the paper for lots more detail on this). Finally, the method was then run on the set of 240K US patents with medical classification codes (A61K*, excluding dental, cosmetic, antibodies etc), resulting in a set of 5.9m molecules that form closely related chemical series (65.3% of the total molecules). As mentioned above, this data set can be downloaded from SureChEMBL.

We'd be keen to hear from anyone who finds this data useful; we are actively exploring a number of different ways to improve the SureChEMBL system including the accuracy of its annotations. 

Comments

Popular posts from this blog

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

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

  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 train a single neural network as a binary multi-label classifier that will output the probability of activity/inactivity for each of the targets (tasks) for a given q

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

Webinar: using an API to access ChEMBL

  If you use ChEMBL via the interface and are interested in programmatic approaches then join  our  webinar   on November 10th @ 15:30 to find out more ! In this webinar, we'll provide an overview of the ChEMBL and UniChem APIs and work through some common examples. In the meantime, don’t forget that we have further documentation on our  web services  as well as a recent ChEMBL  webinar, a Blog and series of  FAQs .  Questions? Send us a message through the  Helpdesk .