We're still playing around with data visualisation, and the experiment of this week focuses on the scientific literature and is designed with tablet devices (such as the iPad or the Nexus 7) and smartphones in mind. The application is a re-thinking of PubMed's search interface and you can get to play with it here at http://pubmed-square.org/
We have a position in the group in the area of ion-channel structural pharmacology - mapping known ion-channel modulators to sequences and binding sites. This will be in partnership with Pfizer, and the role will involve time spent both at the EBI and at Pfizer's labs in the Cambridge UK area - so a great opportunity to pick up some industrial experience.
If you are interested, please get in touch by December 15th 2012, when we will shortlist candidates for interview.
There are some fantastic initiatives in Open Drug Discovery going on at the moment. I for one, are convinced that we are on the cusp of a large structural change in drug discovery, and like at the beginning of all revolutions, the future is not clear, and we all a little bit excited and nervous at the same time. One of the commonly quoted benefits of an Open strategy is that it can avoid duplication, and if you avoid duplication, it means that you get to the goal, faster and cheaper (since other researchers can explore alternative approaches), and there is no repetition. There, you've just read it, and it's quite seductive isn't it?
I've never quite bought this "avoid duplication" argument for the following three reasons. (I should declare my political/philosophical hand here, I have a very deep rooted empathy with the concept of The Free Market. Not the goofy, fudged form that we've had in Western Economies for some time - but that really is a different story, for another time).
1) Scientists are not perfect and they mess things up now and then. The "no duplication" strategy places a lot of weight on the capabilities of a single group, who may not follow the best decision making, have the best approach to data analysis/design etc. There is a lot of discussion at the moment in the literature of the non-repeatability of key pharmacology data, to not have several parallel attempts at a problem seems a little rash given the probably high likelihood of individual failure. If an individual group has a likelihood of 0.6 of getting something done within a given time and given funding. Two groups (with the same likelihood of success/failure) in parallel have a probability of getting it done of 0.84. Simples.
2) Competition is well established to be one of the major drivers of rapid completion in almost all endeavours of life; if you have someone breathing down your neck, potentially scooping you on a paper, you think in a different way, and tend to stay focussed on the task in hand. Given a finite time to complete a piece of work with preplanned and coordinated deliverables, the work miraculously fills the time and funding available.
3) Who will take the decisions over non-duplication? Effectively saying you will not work on this compound series, and another group will, and will people abide with the decisions? We all know that grant committees are useless (unless we are on them of course), and without a lot of process transparency, things could rapidly descend into slow chaos and confusion.
However, I think the arguments for rapid data sharing are very very strong, primarily because they increase liquidity and transparency in the market, and allow market participants to take more rational decisions on the allocation of their resources (individual labs and funders). For me this is the biggest single reason for data sharing (i.e. it actually increases competition, not decreases it). The Free Market of Knowledge in Drug Discovery will drive participants to their best composite roles, based on their abilities.
Here is a review article on cheminformatics, written as an orientation piece for people from a computational sciences background.
%T Cheminformatics
%A J.K. Wegner
%A A. Sterling
%A R. Guha
%A A. Bender
%A J.-L. Faulon
%A J. Hastings
%A N. O'Boyle
%A J. Overington
%A H. Van Vlijmen
%A E. Willighagen
%J Communications of the ACM
%V 55
%I 11
%P 65-75
%O DOI:10.1145/2366316.2366334
Yet another GPCR structure - PDBe code 4grv, the rat neurotensin receptor. The link to the paper is here.
Update - on the first revision of the post, I had word blindness and listed the species as human - it is in fact rat.
%T Structure of the agonist-bound neurotensin receptor
%A J.F. White
%A N. Noinaj
%A Y. Shibata
%A J. Love
%A B. Kloss
%A F. Xu
%A J. Gvozdenovic-Jeremic
%A P. Shah
%A J. Shiloach
%A C.G. Tate
%A R. Grisshammer
%J Nature
%V 490
%P 508–513
%D 2012
%O doi:10.1038/nature11558
In order to provide ChEMBL users with a persistent and citable link to datasets that have been deposited in ChEMBL we have started registering DOIs (Digital Object Identifiers) for these datasets. Many of you will be familiar with the use of DOIs as identifiers for journal articles but they can be used for any document that you want to permanently identify and share with others. By doing this we are providing people with a way of citing a deposited dataset in exactly the same way as you would a scientific publication.
We are also hoping that by issuing DOIs for deposited data we will encourage people to contribute additional data to the ChEMBL database as the DOI will provide them with a permanent way to reference their contribution, for example by using the DOI in a subsequent publication.
At the moment we have DOIs for four of the deposited datasets in the ChEMBL database. Two are results from screens on the GSK PKIS set and two are datasets measured as part of DNDi but we expect these to increase. These datasets and their DOIs are shown below.
CHEMBL_ID
Description
DOI
CHEMBL1961873
Compounds: GSK PKIS; Assays: Nanosyn kinase panel
10.6019/CHEMBL1961873
CHEMBL2007661
Compounds: GSK PKIS; Assays: UNC Frye lab
10.6019/CHEMBL2007661
CHEMBL1857833
Screening and optimization of specific chemical series against human African Trypanosomiasis (HAT)
10.6019/CHEMBL1857833
CHEMBL1862790
Optimisation of fenarimol series for the treatment of Chagas disease
We've just co-authored with a collaborator from GSK an editorial on Open Data available here....
%T Open data for drug discovery: learning from the biological community.
%A A. Hersey
%A S. Senger
%A J.P. Overington
%J Future Medicinal Chemistry
%D 2012
%I 10
%V 4
%P 1865-1867
%O DOI:10.4155/fmc.12.159
The picture of the Fifty Shades of Grey dog I found on the internet somewhere...
Hot on the heels of this years Nobel Prize for Chemistry awarded for structural studies on GPCRs, there's a brand new GPCR structure - human CXCR1, this time an NMR one, published in Nature. The PDBe code is 2lnl. This brings the total to 16 sequence distinct rhodopsin-like GPCR structures that we now have free access to.
%T Structure of the chemokine receptor CXCR1 in phospholipid bilayers
%A S.H. Park
%A B.B. Das
%A F. Casagrande
%A Y. Tian
%A H.J. Nothnagel
%A M. Chu
%A H. Kiefer
%A K. Maier
%A A.A. De Angelis
%A F.M. Marassi
%A S.J. Opella
%J Nature
%D 2012
In order to provide ChEMBL users with a persistent and citable link to datasets that have been deposited in ChEMBL we have started registering DOIs (Digital Object Identifiers) for these datasets. Many of you will be familiar with the use of DOIs as identifiers for journal articles but they can be used for any document that you want to permanently identify and share with others. By doing this we are providing people with a way of citing a deposited dataset in exactly the same way as you would a scientific publication.We are also hoping that by issuing DOIs for deposited data we will encourage people to contribute additional data to the ChEMBL database as the DOI will provide them with a permanent way to reference their contribution, for example by using the DOI in a subsequent publication.At the moment we have DOIs for four of the deposited datasets in the ChEMBL database. Two are results from screens on the GSK PKIS set and two are datasets measured as part of DNDi but we expect these to increase. These datasets and their DOIs are shown below.CHEMBL_IDDescriptionDOICHEMBL1961873Compounds: GSK PKIS; Assays: Nanosyn kinase panel10.6019/CHEMBL1961873CHEMBL2007661Compounds: GSK PKIS; Assays: UNC Frye lab10.6019/CHEMBL2007661CHEMBL1857833Screening and optimization of specific chemical series against human African Trypanosomiasis (HAT)10.6019/CHEMBL1857833CHEMBL1862790Optimisation of fenarimol series for the treatment of Chagas disease10.6019/CHEMBL1862790The DOIs can be resolved to the ChEMBL Document Report Card from the DOI.org website http://dx.doi.org/10.6019/CHEMBL1961873
We've just co-authored with a collaborator from GSK an editorial on Open Data available here....
