Skip to main content

Query Privacy in ChEMBL


We have been asked several times for all the user-generated queries of ChEMBL - i.e. the structures sketched in to the interface that are then searched against the database. We will not (and in fact, physically can't) share these. Sorry. It is against both our institutional privacy policy, and standard Terms of Use, and also we've engineered the app to avoid us 'storing' any of this information where at all possible (e.g. in avoiding /tmp type fluff, minimizing residency time in caches, etc.).

There are clearly some advantages in pooling or analysing website search data - it highlights interesting trends, something becoming more interesting to a user community can spot emerging events, etc. It can alert to flu outbreaks (there was a Science paper from google on this, don't have the reference handy though - you may be able to find it with google though.....). There is a huge interest in many sites that I use in tracking and analysing query terms and usage patterns, and in some contexts this is just the thing to do - like when ebay teases me (and surely of all the tortured obsessive souls on the planet, it is just me and me alone) with a rare phosphor or perforation machin variant I don't have.

The types of query that people perform can clearly also be used to develop ways of improving a website, or specifically the performance of search queries - and for algorithm development this information can be like gold-dust. There are now many chemical fingerprint systems available, and adapting the features/structures of these to typical user queries is really valuable in their development.

There are essentially two distinct aspects to user's expectations/rights of privacy when using a website like ChEMBL.

  • There is a personal privacy issue - 'why is John Overington interested in compounds for the treatment of obesity?'. This is an primarily an embarrassment sort of thing ('hey, is this guy a bit chubby?'), or maybe a commercially sensitive thing ('he's interested in obesity stuff; heh, let's raise the price for him', or 'let's show him some adverts for chips', or 'let's contact his rival and let them know he's interested in his weight'). These latter things are behind the feature where you first search for a flight and the price is great, then the next time you look, it's gone up - allegedly.
  • There's a more fundamental IP issue though -  The simple disclosure of a search term can be commercially damaging, and potentially stop the development of life-saving therapies. The simplest case is chemical structure and drug patents. The most important patent claim in drug discovery is to have composition of matter (and don't get all hissy over pharma misusing the patent system, since patents are absolutely essential for the development of new medicines, the treatment of disease, improvement of food supplies, for funding future R&D, for a source of employment, license revenues to Universities, and taxation revenues, etc). This composition of matter is a claim of a novel chemical structure, that no-one has disclosed before, and it is useful for something. If the structure is not novel, then the patent can be readily invalidated.
Hopefully, you'll understand our reasons for maintaining both user and query privacy.

For an extra clear clarification - we do not, and cannot examine queries of users ourselves within the development team here at the EBI. In case you read the above text as sharing stuff solely with third parties.

Your use of ChEMBL is private, and always will be.


Comments

Bio to Chem said…
John, in regard to your second point there is (unless anyone knows otherwise) no patent case law precedent for a successful composition of matter opposition or invalidation based on the interception of chemical (or sequence for that matter) database queries. Strictly speaking the issue is the public exposure thereof in silico. Ipso facto I'm not sure your (or anyone els's) server cache would count as this in court (hacking in would be criminal interception). Until such time as a test case is prosecuted successful the risk remains close to zero, compared to, say, putting your lead structure on a poster.
jpo said…
You are right. There are some legal defences for 'accidental' disclosure, and also for malicious interception sort of thing. But these seem to go back in spirit to the olden days of real physical post and not electronic transmission.

But I think you misread the post - or a lot more likely I wasn't clear.

The point I was making that you can lose novelty by 'publishing' the query list. The sort of thing I mean is sharing the query list with 'the public', making the query list downloadable, opening up a searchable database of the queries, etc.

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

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

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