A paper entitled Promiscuous 2-Aminothiazoles (PrATs): A Frequent Hitting Scaffold appeared in J Med Chem recently DOI, in which they describe the promiscuous nature of 2-aminothiazoles in screens.
Exemplified by 4-phenylthiazol-2-amine being identified as a hit in 14/14 screens against a diverse range of protein targets, suggesting that this scaffold is a poor starting point for fragment-based drug discovery
I thought I'd check how often this substructure appears in the published fragments database, indeed currently 43 of the 903 published fragments contain this substructure. Further investigation identifies a total of 63 amino-substituted 5-membered heterocycles, and there are 167 fragments in which there is an amino group on an aromatic ring (mainly heterocycles).
It should also be noted however that there are 64 structures in the DrugBank database that also contain a 2-aminothiazole, so whilst promiscuous they can be developed into drugs.
So whether they are privileged structures or troublesome promiscuous hits is probably in the eye of the beholder, caveat emptor.
At the end of each year I take the opportunity to look at the website analytics to see what parts of the website are the most popular. Overall there was a 15% increase in the number of page views up to 75,000. Average time on a page was 2 mins suggesting the content is engaging with the viewers.
Nine of the top ten most popular pages were from the Drug Discovery Resources Pages which I am delighted to see, since it suggests that the work entailed in putting the resources together is worthwhile.
The most viewed pages were
- Distribution and Plasma Protein Binding
- Calculating Physicochemical Properties
- Molecular Interactions
- Fragment based screening
The most popular posts to the news/comments feed were
Registration for the Fragments 2015 Meeting is open.
5th RSC-BMCS Fragment-based Drug Discovery meeting Sunday to Tuesday, 22nd to 24th March 2015 at Churchill College, Cambridge, UK
Always a very popular and informative meeting, the agenda is now finalised and looks excellent.
I often get asked to help with the analysis of high-throughput screening results and one of the first filters I run as part of the hit identification is to flag for PAINS (Pan Assay Interference Compounds) first described by Baell et al DOI and subsequently summarised in an excellent Nature comment.
Academic researchers, drawn into drug discovery without appropriate guidance, are doing muddled science. When biologists identify a protein that contributes to disease, they hunt for chemical compounds that bind to the protein and affect its activity. A typical assay screens many thousands of chemicals. ‘Hits’ become tools for studying the disease, as well as starting points in the hunt for treatments.
These molecules — pan-assay interference compounds, or PAINS — have defined structures, covering several classes of compound. But biologists and inexperienced chemists rarely recognize them. Instead, such compounds are reported as having promising activity against a wide variety of proteins. Time and research money are consequently wasted in attempts to optimize the activity of these compounds. Chemists make multiple analogues of apparent hits hoping to improve the ‘fit’ between protein and compound. Meanwhile, true hits with real potential are neglected.
In the supplementary information they provided the corresponding filters in Sybyl Line Notation (SLN) format, however they have also been converted to SMARTS format and incorporated in sieve file for use in filtering compound collections. If you are a Vortex user then there is also a Vortex script available, filters are also available for Knime and now it is even available on mobile devices with MolPrime+.
It is probably not until you have been involved in multiple small molecule screens that you appreciate the number of ways that false positives can occur and just how much valuable time and resources can be wasted following them up. Indeed it may be for the more difficult targets the majority of hits seen may be false positives. Flagging PAINS is now such a well developed tool that it would be fool hardy not to include it.
The Royal Society of Chemistry have just published The Handbook of Medicinal Chemistry: Principles and Practice (Rsc Smart Materials)
This book was created to support the hugely successful Medicinal Chemistry Summer School run by the RSC.
Each chapter includes expert advice to not only provide a rigorous understanding of the principles being discussed, but to provide useful hints and tips gained from within the pharmaceutical industry. This expertise, combined with project case studies, highlighting and discussing all areas of successful projects, make this an essential handbook for all those involved in pharmaceutical development.
A free app has been created in collaboration with the editors of the book. The Medicinal Chemistry Toolkit provides a suite of resources to support the day to day work of a medicinal chemist
I’ve updated the page of commercial fragment collections, probably the most significant change is that an increasing number of companies are now offering fragment collections with experimentally measured solubilities.
A number of the fragment collections have also reduced in size, perhaps reflecting the more stringent selection requirements.
I just received details of this competition and I thought I’d mention it here.
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The deadline for the first-round online application is Monday, 1 December 2014. Learn more at the OneStart website.
I’ve updated the CYP interactions page, in particular I’ve added details of the WhichCyp server.
Prediction of Cytochromes P450 Inhibition, Bioinformatics, 2013, 29, 2051-2052 WhichCyp, a tool for prediction of which cytochromes P450 isoforms (among 1A2, 2C9, 2C19, 2D6 and 3A4) a given molecule is likely to inhibit. The models are built from experimental high-throughput data using support vector machines and molecular signatures.
A recent paper from Douglas Kell et al DOI has provoked much discussion, especially since it was highlighted on In the Pipeline. The authors suggest that similarity to a human metabolite may be a useful as an indication of how “drug like” a molecule might be.
We exploit the recent availability of a community reconstruction of the human metabolic network (‘Recon2’) to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) ‘nearest’ human metabolite. This suggests a ‘rule of 0.5’ mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This ‘rule of 0.5’ may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.
Whilst this represents an interesting observation I was rather concerned about the choice of a Tanimoto coefficient of 0.5, and decided to repeat the analysis.
The recon-2 dataset was downloaded as a Matlab file, this was exported as a plain text file and Rajarshi Guha converted them to SMILES strings and removed duplicates (and did a comparison with PAINS). I imported these structures into a MOE database and then used a SVL script to compare the recon2 with several other datasets. This included DrugBank that includes details of just under 7000 drug entries, a cleaned up subset of leadlike molecules from Zinc, and BindingDB a public, web-accessible database of measured binding affinities I downloaded in 2008. The datasets were first compared to each other using the MACCS fingerprints with a Tanimoto cutoff of 0.5.
As the table above shows using a Tanimoto coefficient of 0.5 indeed 90% of the molecules in DrugBank are similar to a molecule in recon2, however the same is true for Zinc and BindingDB, indeed at a Tanimoto coefficient of 0.5 all the datasets are pretty similar.
If we increase the Tanimoto coefficient to 0.85 we start to see some resolution, recon2 looks to have more overlap with DrugBank than with either Zinc or BindingDB. However this may simply be a reflection of the fact that DrugBank contains a significant proportion of natural product derived compounds.
The key question of course is “Does this help us to identify compounds that are likely to fail in development?”. It would be really useful to compare with successful drugs and those that fail in development however I’m not aware of any dataset of of failed drug candidates (if anyone knows of one please let me know). However to in an effort to perhaps get some insight I’ve compared the recon2 set with a dataset of drugs that have been withdrawn (for a variety of reasons). As might be expected using a Tanimoto coefficient of 0.5 offers little discrimination. Increasing to 0.85 it looks like there might be a signal there, but the dataset is too small for firm conclusions.
In summary, this limited exploration suggests there may be something worth following up, but that a Tanimoto of 0.5 simply offers little discrimination.