More details from the announcement
For those of you that are already SureChem users you will be familiar with the functionality and how it works; but for those that weren't SureChEMBL takes feeds of full text patents, identifies chemical objects from either the in-line text or from images and adds 2-D chemical structures. This is then loaded into a database and is searchable by chemical structure, so you can do substructure, similarity searching and so forth - all the good things you'd expect from a chemical database. This chemical search functionality is unavailable from the public, published patent documents, and is really essential for anyone seriously using the patent literature. Oh, and the system does this live, so as patents are published, they are processed and added to the system - the delay between publication and structures being available in SureChEMBL is about a day when converted from text, and a few days when converted from image sources
I’ve just updated the aromatic bioisosteres page to include the bicyclo[1.1.1]pentane replacement for phenyl described in a recent publication DOI.
I’ve continued to collect details of fragment based screening hits that have been reported in the literature. There are now over 600 hits reported for 113 different targets culled from over 160 publications. I’ll update the calculated properties for those compounds in due course. I was interested in seeing if the physicochemical profiles are different depending on the type of target, however as the plot below shows, the majority of those hits have been identified against enzyme targets so I think I’ll need more data before any meaningful conclusions can be made.
In contrast when looking at the screening technology used a variety of technologies have afforded a substantial number of hits, when I’ve abstracted the latest batch of papers I’ll have a look at the profiles of the compounds identified using each technology.
Finding the data is getting more of a challenge, it seems as fragment screening becomes more mainstream it is often not mentioned in the title or abstract. So if you have recently published a relevant paper if you could send me the reference or even a pdf I’d be very grateful.
The closing date for the next round of the Wellcome Trust Seeding Drug Discovery initiative is November 8 2013.
Funding to facilitate early-stage small-molecule drug discovery. The awards help applicants with a potential drug target or new chemistry embark on a programme of compound discovery and/or take later stage projects towards clinical trials. The aim of Seeding Drug Discovery is to develop drug-like, small molecules that will be the springboard for further research and development by the biotechnology and pharmaceutical industry in areas of unmet medical need.A two-point entry system has been introduced to enable projects at an earlier stage in development to be competitive for funding as well as to progress later-stage projects further towards clinical trials.
I’ve just updated the section on distribution and plasma protein binding in the Drug Discovery Resources.
At the 17th RSC-SCI Medicinal Chemistry Conference in Cambridge Alexander Pasternak (Merck) gave an excellent talk on their work to identify a potent and selective ROM-K inhibitor as novel diuretics. The ROM-K potassium channel is a member of the inward rectifier family of potassium channels expressed in two regions of the kidney: thick ascending loop of Henle and cortical collecting duct DOI, ROMK participates in potassium recycling across the luminal membrane which is critical for the function of the Na+/K+/2C1" co-transporter, the rate- determining step for salt reuptake in this part of the nephron. At the cortical collecting duct , ROMK provides a pathway for potassium secretion that is tightly coupled to sodium uptake through the amiloride sensitive sodium channel. This makes ROM-K an attractive potassium sparing diuretic target.
To cut a long story short Merck ran a HTS campaign (actually I think they ran two) and the only hit is shown below.
As I am sure all medicinal chemists are aware nitro groups, in particular aromatic nitro groups are well known to be reduced in vivo yielding hydroxylamines and nitrosoamines that are highly reactive species and are known carcinogens. So whilst one nitro in the hit is bad imagine how it feels to have two!
The Merck group however followed this lead up and managed to identify several bioisosteric replacements for the nitro group,
Interestingly there have been two other reported hits for the same target, and these also include nitrobenzenes.
These structures underline the importance of the arylnitro group but also raise a couple of interesting questions, whether nitro compounds should be removed from screening collections? In addition, given the structure of ion channels is often a parallel array of identical proteins forming a pore through the membrane perhaps we should try to populate screening collections with palindromic structures that might bind linking two chains?
I’ll add these to the bioisosteres section at the weekend.
I’ve now updated the physicochemical property profiles of all the fragment collections I have access to, including the categorisation into rod-, disc- or sphere-like shapes I described last week.
I thought it might be interesting to generate a plot of all 170,000 fragments to look at the distribution. I actually viewed the results in Vortex as shown below. This tool makes it easy to colour by “shape” and also allows me to highlight a few structures that appear at the extremism of the plot.
I recently updated the fragment collections page this included updating the physicochemical property profiles adding npmi (Normalized ratio of principle moments of inertia) as described by Sauer WH, Schwarz MK (2003) Molecular shape diversity of combinatorial libraries: A prerequisite for broad bioactivity. J Chem Inf Comput Sci 43:987–10030. DOI As the image below shows this gives a view of the shape of the molecules as to whether they are rod, disk or sphere like.
Whilst this works very well for individual compounds or small libraries the plot becomes a blur of overlapping points for larger collections and it is not really possible to compare collections. Whilst it may be possible to generate a single number as the “average” of each collection I’m not sure how useful it would be. So with help from Matt I decided to divide the plot into three sections as shown below.
The centre point (c1, c2) was calculated using ( 0.5, (2sqrt(0.5) + 0.5)/(2sqrt(0.5) + 1) ) which is about (0.5, 0.793).
Each of the points in the plot was then assigned to a category using:
If a point is below both lines then: (0.5 - 0.25) * (npmi2 - 0.75) - (0.793 - 0.75) * (npmi1 - 0.25) < 0 and (0.5 - 0.75) * (npmi2 - 0.75) - (0.793 - 0.75) * (npmi1 - 0.75) > 0 then it is disc-like.
If not, then it is rod-like if npmi1 < 0.5 and sphere-like if npmi1 > 0.5.
The result for a 35,000 compound collection are shown below, with the points colour-coded by the assigned category.
We can then create a categorical plot as shown below.
I plan to update all the physicochemical profiles of all the fragment collections next week.
I just added the dataset from the 3D Fragment Library Consortium.
The 3D Fragment Consortium brings together UK-based not-for-profit drug discovery institutes and academic groups, working in partnership to build a collection of chemically diverse molecules with a particular focus on fragments that incorporate 3D structure. The consortium is looking to collaborate with other research groups to expand the collection and make it available for screening against new biological targets to help kick-start hit discovery programmes and provide a foundation for a vibrant pre-competitive drug discovery network across the UK. The 3D Fragment Consortium has identified a foundation library of 170 fragments to commence their screening activities.
I’ve added this fragment library profile to the 22 other collections previously calculated:
It is obviously early days yet but it will be interesting to see how this develops.