Archive for the ‘funding’ tag
Update (07/28/16): DrugBank/OMx have updated the licensing conditions for DrugBank data in response to concerns raised earlier by various people and groups. See here for a detailed response from Craig Knox
A few days back I came across, via my Twitter network, the news that DrugBank had changed their licensing policy to CC BY-SA-NC 4.0. As such this is not a remarkable change (though one could argue about the NC clause, since as John Overington points out the distinction between commercial and non-commercial usage can be murky). However, on top of this license, the EULA listed a number of more restrictive conditions on reuse of the data. See this thread on ThinkLab for a more detailed discussion and breakdown.
This led to discussion amongst a variety of people regarding the sustainability of data resources. In this case while DrugBank was (and is) funded by federal grants, these are not guaranteed in perpetuity. And thus DrugBank, and indeed any resource, needs to have a plan to sustain itself. Charging for commercial access is one such approach. While it can be problematic for reuse and other Open projects, one cannot fault the developers if they choose a path that enables them to continue to build upon their work.
The British Pharmacological Society (BPS) has committed support for GtoPdb until 2020 and the Wellcome Trust support for GtoImmuPdb until 2018. Needless to say the management team (between, IUPHAR, BPS and the University of Edinburgh) are engaged in sustainability planning beyond those dates. We have also just applied for UK ELIXIR Node consideration.
So it’s nice to see that the resource is completely free of any onerous restrictions until 2020. I have no doubt that the management team will be working hard to secure funding beyond that date. But in case they don’t, will their licensing also change to support some form of commercialization? Certainly, other resources are going down that path. John Overington pointed to BioCyc switching to a subscription model
— John P. Overington (@johnpoverington) May 9, 2016
So the sustainability of data resources is an ongoing problem, and will become a bigger issue as the links between resources grows over time. Economic considerations would suggest that permanent funding of every database cannot happen.
So clearly, some resources will win and some will lose, and the winners will not stay winners forever.
Open source software & transferring leadership
However in contrast to databases, many Open Source software projects do continue development over pretty long time periods. Some of these projects receive public funding and also provide dual licensing options, allowing for income from industrial users.
However there are others which are not heavily funded, yet continue to develop. My favorite example is Jmol which has been in existence for more than 15 years and has remained completely Open Source. One of the key features of this project is that the leadership has passed from one individual to another over the years, starting I think with Dan Gezelter, then Bradley Smith, Egon Willighagen, Miguel Rojas and currently Bob Hanson.
Comparing Open software to Open databases is not fully correct. But this notion of leadership transition is something that could play a useful role in sustaining databases. Thus, if group X cannot raise funding for continued development, maybe group Y (that obviously benefits from the database) that has funding, could take over development and maintenance.
There are obvious reasons that this won’t work – maybe the expertise resides only in group X? I doubt this is really an issue, at least for non-niche databases. One could also argue that this approach is a sort of proto-crowdsourcing approach. While crowdsourcing did come up in the Twitter thread, I’m not convinced this is a scalable approach to sustainability. The “diffuse motivation” of a crowd is quite distinct from the “focused motivation” of a dedicated group. And on top of that, many databases are specialized and the relevant crowd is rather small.
One ultimate solution is that governments host databases in perpetuity. This raises a myriad issues. Does it imply storage and no development? Is this for all publicly funded databases? Or a subset? Who are the chosen ones? And of course, how long will the government pay for it? The NIH Commons, while not being designed for database persistence, is one such prototypical infrastructure that could start addressing these questions.
In conclusion, the issue of database sustainability is problematic and unsolved and the problem is only going to get worse. While unfortunate for Open science (and science in general) the commercialization of databases will always be a possibility. One hopes that in such cases, a balance will be struck between income and free (re)usage of these valuable resources.
… In my opinion, it is not a “hot” field, though, in part for some of the reasons mentioned in the post – particularly the fact that the data in the field is mostly proprietary and/or secret. So they hurt themselves by that behavior. But the other reason I don’t think it is moving that fast is that, unlike bioinformatics, chemoinformatics is not being spurred by dramatic new technological advances. In bioinformatics, the amazing progress in automated DNA sequencing has driven the science forward at a tremendous pace …
I agree with Steven and others that cheminformatics is not as “hot” as bioinformatics, based on varying metrics of hotness (groups, publications, funding, etc.). However I think the perceived lack of popularity stems from a number of reasons and that technological pushes are a minor reason. (Andrew Dalke noted some of these in a comment).
1. Lack of publicaly accessible data – this has been mentioned in various places and I believe is a key reason that held back the development of cheminformatics outside industry. This is not to say that academic groups weren’t doing cheminformatics in 70’s and 80’s, but we could’ve had a much richer ecosystem.
In this vein, it’s also important to note that just public structure data, while necessary, would likely not have been sufficient for cheminformatics developemnt. Rather, structure and biological activity data are both requred for the development of novel cheminformatics methodologies. (Of course certain aspects of cheminformatics are are focused purely on chemical structure, and as such do fine in the abensce of publically accesssible activity data).
2. Small molecules can make money directly – this is a primary driver for the previous point. A small molecule with confirmed activity against a target of interest can make somebody a lot of money. It’s not just that one molecule – analogs could be even more potent. As a result, the incentive to hold back swathes of structure and activity data is the financially sensible approach. (Whether this is actually useful is open to debate). On the other hand, sequence data is rarely commercialiable (though use of the sequence could be) and hence much easier to release.
3. Burden of knowledge – as I mentioned in my previous post, I believe that to make headway in many areas of cheminformatics requires some background in chemistry, sincce mathematical abstractions (cf graph representations) only take you so far. As Andrew noted, “Bioinformatics has an “overarching mathematical theory” because it’s based very directly on evolution, encoded in linear sequences“. As a result the theoretical underpinnings of much of bioinformatics make it more accessible to the broader community of CS and mathematics. This is not to say that new mathematical developments are not possible in cheminformatics – it’s just a much more complex topic to tackle.
4. Lack of federal funding – this is really a function of the above three points. The idea that it’s all been done in industry is something I’ve heard before at meetings. Obviously, with poor or no federal funding opportunities, fewer groups see cheminformatics as a “rewarding” field. While I still think the NIH’s cancellation of the ECCR program was pretty dumb, this is not to say that there is no federal funding for cheminformatics. Applications just have to be appropriately spun.
To address Stevens’ point regarding technology driving the science – I disagree. While large scale synthesis is possible in some ways (such as combinatorial libraries, diversity oriented synthesis etc.), just making large numbers of molecules is not really a solution. If it were, we might as well generate them virtually and work from the SMILES.
Instead, what is required is large scale activity measurements. And there have been technology developments that allow one to generate large amounts of structure-actvity data – namely, High Throughput Screening (HTS) technologies. Admittedly, the data so generated is not near the scale of sequencing – but at the same time, compared to sequencing, every HTS project usually requires some form of unique optimization of assay conditions. Added to that, we’re usually looking at a complex system and not just a nucleotide sequence and it’s easy to see why HTS assays are not going to be at the scale of next gen sequencing.
But, much of this technology was relegated to industry. It’s only in the last few years that HTS technology has been accesible outside industry and efforts such as the Molecular Libraries Initiative have made great strides in getting HTS technologies to academics and more importantly, making the results of these screens publicaly available.
As a bioinformatics bystander, while I see reports of next gen sequencing pushing out GBs and TBs of data and hence the need for new bioinformatics methods – I don’t see a whole lot of “new” bioinformatics. To me it seems that its just variations of putting together sequences faster – which seems a rather narrow area, if that’s all that is being pushed by these technological developments. (I have my asbestos underwear on, so feel free to flame)
Certainly, bioinformatics is helped by high profile projects such as the Human Genome Project and the more recent 1000 Genomes project which certainly have great gee-whiz factors. What might be an equivalent for cheminformatics? I’m not sure – but I’d guess something on the lines of systems biology or systems chemical biology might be a possibility.
Or maybe cheminformatics just needs to become “small molecule bioinformatics”?