Skip to main content

To Remove Or Not To Remove - That Is The Question


During the course of standard compound curation, I come across problem inorganic compounds. An example of these are Cisplatin and Transplatin. These compounds only differ in the orientation of their complex bonds but complex bonds cannot be drawn in a standard molfile without causing InChI issues. At the  moment, they are kept separate by showing standard bonds between the Pt, Cl and NH3 in Cisplatin, but we have removed the bonds altogether for Transplatin. This is not an ideal situation, nor an accurate structural representation.

Another example is the compound, below left, and how it should look as a complex, right, from the paper:



At the moment, there are approximately 1,800 cases like this, which only accounts for 0.15% of the entire ChEMBL compound set.

What we are proposing to do is to remove the structures for these complex compounds and to keep only their names and all of the associated biological data. This would then treat them in a similar way to the antibodies and large peptides that we store in ChEMBL.

So, we have set up an online private Doodle Poll for you, our users, to have your vote on whether we should remove the structures and keep the biological data, or leave them as they are.

All comments are welcome.


Louisa

Comments

Noel O'Boyle said…
Hi Louisa, you didn't say exactly whtat the proble is. Is it not possible to use the InChI software to generate different InChIs for cis and trans-platin? The "don't disconnect metals" option (/RecMet) is worth trying.

BTW, the link to the compound above is a different structure than shown in the image.
Louisa said…
Hi Noel

No, you can't generate different InChIs for trans and cisplatin.

The structures shown were taken from the original paper and ChEMBL. It was used to illustrate how covalent bonds are being used to show complex compounds where co-ordinate dative bonds should be used. Unfortunately, coordinate bonds do not give an acceptable InChI.
In the case shown on the post, covalent bonds have been used in place of coordinate bonds and so the NH3 group and NH2R group have lost a hydrogen so as not to have a charge.
As there are only few cases of this, we would like to remove the structure but keep the data. That way, we are not storing compounds with 'incorrect' bond types.

thanks
Louisa

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

ChEMBL 26 Released

We are pleased to announce the release of ChEMBL_26 This version of the database, prepared on 10/01/2020 contains: 2,425,876 compound records 1,950,765 compounds (of which 1,940,733 have mol files) 15,996,368 activities 1,221,311 assays 13,377 targets 76,076 documents You can query the ChEMBL 26 data online via the ChEMBL Interface and you can also download the data from the ChEMBL FTP site . Please see ChEMBL_26 release notes for full details of all changes in this release. Changes since the last release: * Deposited Data Sets: CO-ADD antimicrobial screening data: Two new data sets have been included from the Community for Open Access Drug Discovery (CO-ADD). These data sets are screening of the NIH NCI Natural Product Set III in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296183, DOI = 10.6019/CHEMBL4296183) and screening of the NIH NCI Diversity Set V in the CO-ADD assays (src_id = 40, Document ChEMBL_ID = CHEMBL4296182, DOI = 10.601