Skip to main content

Striving for Perfect Representation of Chemical Structures – is this possible?

It probably goes without saying that at ChEMBL, we have a desire to make all our data as accurate and useful as possible. With this in mind we have spent many hours over the last few years trying to curate, in particular, the structures of marketed drugs and clinical candidates. We aren’t alone in this and more than 5 years ago people were coming across the same problems as highlighted in this blog post by ChemConnector on Fluvastatin

Our drug curation is an ongoing and probably a never-ending task but to be honest it has proved a lot more difficult than we expected. This is for several reasons:

Firstly, where to go to find the definitive structure of a molecule? One would have thought this would be easy but even the sources such as INN and USAN don’t always agree. For example for Telavancin the USAN_data_sheet shows a difference in the nitrogen and carbon counts in the structure images compared with the images in the INN document (although the molecular formula are the same in both documents).

Secondly, while molfiles are our definitive structures and we use standard InChIs to determine uniqueness we see many examples where, as we convert between formats (molfiles, smiles, InChIs) we introduce inconsistencies. This is of course a well-known problem. There are ongoing discussions and initiatives to develop open structure formats and extend InChIs to deal with some of these cases but my sense is this is a long way off.

Lastly, and what seems like an insurmountable problem, we are constrained by the method we use to represent chemical structures. We, at ChEMBL, like many people, use version 2000 molfiles (ref 1). There is no doubt that using v3000 molfiles would solve a number of these problems but it would be very time consuming and costly to do the conversion and therefore probably only feasible for a limited number of ChEMBL structures such as the drug molecules. We are considering this as a long term goal but it would need a wider community buy in to make it worthwhile.  However, we also suspect that many of our users also only use the older molfile version so providing the v3000 format wouldn’t help them. We would be interested in your feedback on which format you use though. Most of the resources we exchange data with (e.g. PubChem, BindingDB) also use v2000 molfiles. There is no doubt that different resources find their own way to cope with the limitations of the file formats and we do too. For example, it would be possible to use non-standard extensions of the datafields in the sd file to indicate this but it would lack real chemical awareness. Also, how one group chooses to use this won’t necessarily be consistent with another group so we are no further forward.

As a consequence of our curation efforts, we have come across an increasing number of challenging molecules for which it would be useful to get the views of our users as to the best way to deal with these. It should also be said here that we are only talking about apparently “simple” rule of 5 compliant organic molecules and several years ago we stopped trying to curate organometallic compounds. We don’t show the structures of these in ChEMBL. The drug cisplatin being a case in point. The v2000 molfile has no way of coding coordination bonds and the standard InChI (ref 2) that we use to define a unique chemical structure can’t distinguish between cis and trans-platin.

Back to the organic molecules though and a few of our dilemmas:

Milnacipran is my favourite and an apparently relatively simple example.  It is a mixture of the 1S,2R and 1R,2S enantiomers (USAN). However, v2000 molfiles don’t deal with relative stereochemistry so we have 3 options:

(1) Show one enantiomer:
(2) Show it as a racemic mixture i.e. no stereochemistry:

(3) Show it as a molfile comprised of two molecules:

Arguably option 3 is the only correct way to do this. However other data providers such as FDA and Drugbank use option 1. In the ChEMBL database we use option 2 so that we can distinguish milnacipran from levomilnacipran USAN (specifically the 1S, 2R isomer) or dextromilnacipran (1R, 2S). Option 1 wouldn’t enable us to distinguish these either in the molfile or the standard InChI.

My logic here for not using option 3 is in thinking about the use people are making of ChEMBL. ChEMBL is not a registration system where option 3 might indeed be needed but it is being used as a source of bioactivity data that can be used for identifying tool compounds, building QSAR models for specific targets etc. Hence wouldn’t users taking our 1.8 million compounds just discard any mixtures such as option 3 would give before starting their analysis given that calculating physicochemical properties etc on mixtures makes little sense?

OK so suppose you disagree and think option 3 is the right thing to do, what would you want us to do for itraconazole? This is described in DailyMed (ref 3) as a “1:1:1:1 racemic mixture of four diastereoisomers (two enantiomeric pairs)”.

Option 3 would give us a mixture of 4 molecules in our v2000 molfile. For example:

Again, we have chosen option 2 as the least bad option i.e just showing it as a racemic mixture.

It seemed as if we had identified a workable and at least internally consistent way of dealing with these structures – until we took a look at the following two examples alpha prodine and beta prodine:

Here we have alphaprodine being a mixture of the (RS,SR) enantiomers:
and betaprodine the (SS,RR) enantiomers:
Hence our use of option 2 fails to distinguish between them! This matters as the two enantiomeric pairs have different biological properties e.g. different analgesic activity (ref 4)

The other example is Met(h)iomeprazine and levomet(h)iomeprazine where the former is a mixture of two enantiomers and the latter one enantiomer or the other (but it isn’t apparently known which - according to INN).

For this example, we have chosen option 2 for metiomeprazine but for levometiomeprazine we show just one of the possible enantiomers.

In summary, no existing solutions are ideal and not everyone agrees on how to do this. In ChEMBL itself we are trying to be consistent within the constraints of the v2000 molfile format but it’s not all done yet. There is however a glimmer of light in this confusion in that our UniChem connectivity match (ref 5) enables matching of these cases across databases. For example using the non stereospecific representation of milnacipran enables matching to this as well as the specific levo- and dextro- milnacipran enantiomers (as well as their salts). Details here.

So, ChEMBL users out there, we’d be interested in what you think. Do you prefer option 1, 2 or 3 or for your use cases or does it make no difference? We can’t promise an instant change but we are interested in what you think. Before you ask we know we have some inconsistencies in ChEMBL for these molecules but we are undecided on what to do and of course time spent on this is less time on other things. If you want to vote on your preferred option you can do so here.

As always if you think we have something wrong in ChEMBL please email and we will endeavour to correct it.

(1) A. Dalby, J.G. Nourse, W. D. Hounshell, A.K.I. Gushurst, D. L. Grier, B.A. Leland and J. Laufer, Description of Several Chemical Structure File Formats Used by Computer Programs Developed at Molecular Design Limited, Chem. Inf. Comput. Sci. 1992, 32, 244-255

(2) InChI - the worldwide chemical structure identifier standard, S Heller, A McNaught, D. Tchekhovskoi and S. Stein, J. Cheminf. 2013, 5

(3) Dailymed entry for Itraconazole

(4) A.H Becket, A.F. Casy and G Kirk – Alpha and Beta Prodine Type Compounds, J. Med. and Pharmaceut. Chem., 1959,1,1-58

(5) J. Chambers, M. Davies, A. Gaulton, G. Papadatos, A. Hersey and J. P. Overington, UniChem: extension of InChI-based compound mapping to salt, connectivity and stereochemistry layers, J. Cheminformatics 2014, 6:43


Popular posts from this blog

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 29 Released

  We are pleased to announce the release of ChEMBL 29. This version of the database, prepared on 01/07/2021 contains: 2,703,543 compound records 2,105,464 compounds (of which 2,084,724 have mol files) 18,635,916 activities 1,383,553 assays 14,554 targets 81,544 documents Data can be downloaded from the ChEMBL FTP site: .  Please see ChEMBL_29 release notes for full details of all changes in this release: New Deposited Datasets EUbOPEN Chemogenomic Library (src_id = 55, ChEMBL Document IDs CHEMBL4649982-CHEMBL4649998): 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 synthesiz

Julia meets RDKit

Julia is a young programming language that is getting some traction in the scientific community. It is a dynamically typed, memory safe and high performance JIT compiled language that was designed to replace languages such as Matlab, R and Python. We've been keeping an an eye on it for a while but we were missing something... yes, RDKit! Fortunately, Greg very recently added the MinimalLib CFFI interface to the RDKit repertoire. This is nothing else than a C API that makes it very easy to call RDKit from almost any programming language. More information about the MinimalLib is available directly from the source . The existence of this MinimalLib CFFI interface meant that we no longer had an excuse to not give it a go! First, we added a BinaryBuilder recipe for building RDKit's MinimalLib into Julia's Yggdrasil repository (thanks Mosè for reviewing!). The recipe builds and automatically uploads the library to Julia's general package registry. The build currently targe

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 curren

New Drug Warnings Browser

As mentioned in the announcement post of  ChEMBL 29 , a new Drug Warnings Browser has been created. This is an updated version of the entity browsers in ChEMBL ( Compounds , Targets , Activities , etc). It contains new features that will be tried out with the Drug Warnings and will be applied to the other entities gradually. The new features of the Drug Warnings Browser are described below. More visible buttons to link to other entities This functionality is already available in the old entity browsers, but the button to use it is not easily recognised. In the new version, the buttons are more visible. By using those buttons, users can see the related activities, compounds, drugs, mechanisms of action and drug indications to the drug warnings selected. The page will take users to the corresponding entity browser with the items related to the ones selected, or to all the items in the dataset if the user didn’t select any. Additionally, the process of creating the join query is no