Skip to main content

Advanced keyword and structure searches with SureChEMBL


Previously in the SureChEMBL series, we described how to access SureChEMBL data in bulk, offline and locally. So, you may ask, what is the point in using the SureChEMBL web interface? Well, how about the unprecedented functionality that allows you to submit very granular queries by combining: i) Lucene fields against full-text and bibliographic metadata and ii) advanced structure query features against the annotated compound corpus - at the same time?

Let’s see each one separately first:

Lucene-powered keyword searching

You may use the main text box for simple keyword-based patent searches, such as ‘Apple’, ‘diabetes’ or even 'chocolate cake' (the patent corpus as a recipe book is a new use-case here). You will get a lot of results and probably a lot of noise. With Lucene fields, you can slice and dice a query by indicating specific patent sections and bibliographic metadata, such as date/year of filing or publication, assignee, patent classification code, patent authority, etc. For example, to search for the term ‘diabetes’ only in the abstract of patents, you can search with:
ab:diabetes

where ab is the Lucene query field for abstract. For a full list of Lucene queries, see here. Furthermore, you can combine these fields with boolean operators (AND, OR, NOT - always in UPPER case) and brackets. For example to find US patents published in 2014 which also mention the word ‘diabetes’ in the title or abstract, you could search with:

(ttl:diabetes OR ab:diabetes) AND pdyear:2014 AND pnctry:US

or even limit it to more med-chem relevant patent hits by using the appropriate IPC hierarchical classification codes:

(ttl:diabetes OR ab:diabetes) AND ic:(C07D AND (A61K OR A61P)) AND pdyear:2014 AND pnctry:US

Is that all? No, you could also use wildcards, such as * and ?, as well as proximity searches:

(ttl:diabet* OR ab:diabet*) AND pdyear:2014 AND pnctry:US

A couple of thing worth pointing out here:
1) in the way described above, you may search not only the chemically-annotated (EP, US, WO, JP patents) or chemically-relevant corpus but any patent within SureChEMBL’s broad coverage, such as French, German, British, Chinese, Australian, Canadian, etc., patents about any topic:

pa:"Apple Inc" AND ab:vehicle AND pnctry:CN

for such cases, just remember to check the 'All authorities' box on the right hand side panel.
2) If the Lucene query syntax seems too complicated, almost the same functionality is available via a more user-friendly field-based widget called Fielded Search:
  

ChemAxon-powered structure searching

To begin with, SureChEMBL provides basic substructure and similarity searches against the currently 17 million chemical structures, powered by ChemAxon’s JChem technology. Some of you may have noticed that we have recently done some refurbishment around the sketchers and we now provide the latest MarvinJS sketcher as the sole source of structure input. We also removed the manual entry box, as it is superseded by functionality described below. Behind the scenes, we use the native ChemAxon inter-conversion functionality to ensure maximum compatibility and minimum information loss during structure conversions. The good news is that you can input a structure in several ways (besides sketching it from scratch), e.g. SMILES, SMARTS, CML, InChI, Molfile and IUPAC/trivial name. Just click and paste your string on the MarvinJS sketcher or open the import dialogue to paste it right there - or even upload a file. More importantly, you may now take advantage of more advanced query features, such as (NOT) atom and bond lists, explicit hydrogens, as well as the Markush-friendly position variation and repetition ranges.

For example, this is a query that combines atom, not atom, and bond lists, as well as explicit hydrogens to control substitution:


Or this one, which combines position variation and linker repetition range:


Again, don't forget that you have additional control over the MW range of the search hits, as well as their exact location in the patent document (title, abstract, claims, description, images/molfiles).


Combined keyword and structure searching

Finally, as mentioned in the beginning, you can easily submit combined keyword and structure queries, such as this one:


...to our knowledge, there's no other freely available patent searching resource or interface out there providing this type of functionality but we're happy to stand corrected...

As usual, for any questions or feedback, drop us a line.


George and Nathan

Comments

Popular posts from this blog

New SureChEMBL announcement

(Generated with DALL-E 3 ∙ 30 October 2023 at 1:48 pm) We have some very exciting news to report: the new SureChEMBL is now available! Hooray! What is SureChEMBL, you may ask. Good question! In our portfolio of chemical biology services, alongside our established database of bioactivity data for drug-like molecules ChEMBL , our dictionary of annotated small molecule entities ChEBI , and our compound cross-referencing system UniChem , we also deliver a database of annotated patents! Almost 10 years ago , EMBL-EBI acquired the SureChem system of chemically annotated patents and made this freely accessible in the public domain as SureChEMBL. Since then, our team has continued to maintain and deliver SureChEMBL. However, this has become increasingly challenging due to the complexities of the underlying codebase. We were awarded a Wellcome Trust grant in 2021 to completely overhaul SureChEMBL, with a new UI, backend infrastructure, and new f

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

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

Using ChEMBL activity comments

We’re sometimes asked what the ‘activity_comments’ in the ChEMBL database mean. In this Blog post, we’ll use aspirin as an example to explain some of the more common activity comments. First, let’s review the bioactivity data included in ChEMBL. We extract bioactivity data directly from   seven core medicinal chemistry journals . Some common activity types, such as IC50s, are standardised  to allow broad comparisons across assays; the standardised data can be found in the  standard_value ,  standard_relation  and  standard_units  fields. Original data is retained in the database downloads in the  value ,  relation  and  units  fields. However, we extract all data from a publication including non-numerical bioactivity and ADME data. In these cases, the activity comments may be populated during the ChEMBL extraction-curation process  in order to capture the author's  overall  conclusions . Similarly, for deposited datasets and subsets of other databases (e.g. DrugMatrix, PubChem), th