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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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.
I’ve updated the pages on bioisosteres to include more examples.
I’ve updated the page on HERG activity, to include a little more information on pharmacophore models.
As someone who regularly reads Derek Lowe’s “In the Pipeline” blog I was taken with the post on The Smallest Drugs in which he highlighted the structures using the arbitrary cutoffs
the molecular weight cutoff was set, arbitrarily, at aspirin's 180. I excluded the inhaled anaesthetics, only allowing things that are oils or solids in their form of use. As a small-molecule organic chemist, I only allowed organic compounds - lithium and so on are for another category.
An interesting selection but I thought it might be interesting to profile the calculated properties, I used the DrugBank Database too ensure I got a more comprehensive dataset and then calculated properties as I have done for the Fragment collections. The results are shown below. Probably the most notable feature is the number that contain ionisable groups, over 60% of the molecules would be predicted to be ionised at physiological pH (note however it does include a couple of natural amino acids). Around 50% contain an aromatic ring (of which 2/3 are heterocycles). There are a couple of structures with more 3D shape (Memantine) but in general they would be classified as disc or rod-like. In general the results don’t look too dissimilar to the Published Fragment Hits.
I’ve updated the page on published fragments, the dataset now includes over 800 published fragments hits abstracted from over 200 publications directed at nearly 130 different molecular targets using 22 different detection technologies and might be expected to give some insight into the type of compounds that appear as hits. With the caveat that the dataset only includes information that has been published.
UK researchers will be granted access to a ‘virtual library’ of deprioritised pharmaceutical compounds through a new partnership between the Medical Research Council (MRC) and seven global drug companies, announced today by Business Secretary Vince Cable.
AstraZeneca, GlaxoSmithKline, Janssen Research & Development LLC*, Lilly, Pfizer, Takeda and UCB will each offer up a number of their deprioritised molecules for use in new studies to improve our understanding of a range of diseases. A full list of available compounds will be published later this year, when UK scientists will be able to apply for MRC funding to use them in academic research projects.
This has the potential to a really exciting resource for scientists to explore the pathways involved in a variety of different diseases, and since the compounds have apparently undergone some development it may provide a boon to those involved in repurposing drugs. Much will of course depend on the compounds offered but perhaps other companies will follow suit.