Top In-Silico Drug Discovery Papers

Question

[Flow]

Which are the top 5 papers that report the discovery of a drug starting from a computer prediction? I mean previous active small molecule compound computer prediction by any bioinformatics method and posterior experimental validation. I guess there are not many

PS: Just discovery of relevant active compound, not optimization

PS2: By "top paper" I mean paper in a high-impact factor journal, higher than 4.0 (according to JCR) or a paper cited a lot of times (more than 50, for example)

[Chris Evelo]

I don't think adding the impact factor really helps, at least not for what you are after. You want to limit your question to one specific domain. There is no reason to expect that journals with the highest overall impact factor will also have the most relevant papers with respect to your topic. Keep in mind that the journal impact factor is determined by the average number of citations for all papers. You would probably want the most cited papers.

[Michael Kuhn]

this should become a community wiki question

[Flow]

ok, go ahead !!!

[Chris Evelo]

The combination of a community wiki with a bounty is a bit strange isn't it? Community wiki's are originally meant to collaboratively reach and answer on a tough question, by editing the question itself. We mostly use it for questions that are interesting, but considered by many of us not to be about bioinformatics. That is probably what Michael meant here (I am not sure I agree). In both cases it doesn't make sense to earn reputation for questions or answers, and in fact you don't get any for a wikified question. For me it is strange that you can even give a bounty for a community wiki.

[Simon Cockell]

I am unclear on who offered the bounty on this question, since the bounty comes from a users own reputation, and 'flow' does not have sufficient rep to award the 300 rep bounty. As a subjective question ("Which are in your opinion...") with no correct answer, the wiki status is certainly warranted in my mind, but the bounty just confuses me.

[Flow]

It is just first course math; I had before 319 reputation, and after offering the bounty I have 19. If you want I can change "which are in your opinion" to "5 papers related to the topic with in a journal with the highest possible impact factor"

[Simon Cockell]

Ah, that helps, didn't know the bounty was taken off before it was awarded to someone, thanks :)

[Chris Evelo]

I don't think that really helps, at least not for what you are after. You want to limit your question to one specific domain. There is no reason to expect that journals with the highest overall impact factor will also have the most relevant papers with respect to your topic.

[Chris Evelo]

I don't think adding the impact factor that really helps, at least not for what you are after. You want to limit your question to one specific domain. There is no reason to expect that journals with the highest overall impact factor will also have the most relevant papers with respect to your topic.

[Chris Evelo]

I don't think adding the impact factor that really helps, at least not for what you are after. You want to limit your question to one specific domain. There is no reason to expect that journals with the highest overall impact factor will also have the most relevant papers with respect to your topic. Keep in mind that the journal impact factor is determined by the average number of citations for all papers. You would probably want the most cited papers.

Answer 1

[Chris Evelo]

OK. I know this is not a real answer to your question but I think you will not want to miss today's news, mentioned in the tweet by @Ananyo below. Ananyo Bhattacharya is Natures chief online editor. Of course this work did not lead to a new drug yet. But it will shake up drug research, and we now at least have the structures to do the prediction with:

RT @Ananyo: Biologists-don't miss @alohalizzie's gr8 news story on the new GPCR structure: Nobel prize-winning stuff http://bit.ly/oDvpqF

For instance for the reason @Ananyo gave in his second tweet about the topic:

RT @Ananyo: This is why I <3 crystallography. The GPCR structure cld explain how cholera toxin kills http://bit.ly/oDvpqF

Since you asked for the actual papers. Here it is: Rasmussen, S. G. F. et al. Nature http://dx.doi.org/10.1038/nature10361 (2011).

[Flow]

yeah, very interesting, but they do not use computer methods to predict anything

[Chris Evelo]

You are right, they just produce the data you would need to do so. That is why I said it is not a real answer. But the chance that more real answers will exist in a few years gets a whole larger this way.

Answer 2

[Chris Evelo]

I think there is also another answer. If you really do find a drug using modelling (and from what I understand a number GPCR agonists and antagonists have been found in pharmaceutical industry in this way, using incomplete models for GPCR) you will not publish that in a scientific journal. You will instead verify your results and then get a patent. Publishing it in another way would be about the stupidest thing you could do. The publication would effectively ruin your chance to get a patent, and without the patent you would not be able to invest in the necessary toxicity studies and clinical trials. The latter is true since any competitor would be able to sell the drug at a lower price since he would not have to invest in the studies needed to get the new drug registered.

Since patents are in fact accessible you might want to search there.

[Niallhaslam]

http://worldwide.espacenet.com/ is a good place to start.

[Flow]

yes, this is a very good point! I will have a look at patents. But there is also people that, as you mention, first fill the patent, and later publish the discovery, and I at least, did not find many papers reporting that

[Flow]

where would you usually try to find these patents? is google patents enough? I admit I have no experience with patents

[Chris Evelo]

In principle you are right. But although I am not a patent expert I don't think you would really do that. Apart from the fact that when you are in pharma and working on a really new substance you probably have more important things to do, the patent and publication text would never be completely identical and the publication could be used to find loopholes in the patent itself. Now looking for such loopholes is what competing companies do all the time, so why would you increase the risk?

Answer 3

[Simon Cockell]

There's a whole issue of Briefings in Bioinformatics dedicated to computational approaches to drug repositioning: http://bib.oxfordjournals.org/content/12/4.toc

Not drug discovery per se, but certainly an area that has shown a lot of promise in response to purely computational approaches.

[Flow]

repurposing is also a very interesting field, but I am looking for novel compound discovery

Answer 4

[Shigeta]

I think Chris has a good point - protein structures are often what you need to have to even start guessing at drug structures, so its a lot easier to think of papers that are really the report of a protein structure.

A couple of examples:

HIV Protease structure in complex with peptide inhibitors. It was such an important target that they synthesized the protein on solid phase for the structure.

COX1 and 2 - primary targets for inflammation (I understand that the organic chemists built the COX inhibitors before the structure was done tho).

Influenza virus haemagglutinin - i think this actually helped spur the development of some flu drugs.

I would be interested to know if there were any purely insilico designed drug targets. Usually even insilico designed libraries of drug candidates are huge (> 100 million) and the computers use the structures of known drugs or of the protein target to throw out the first 80-90% then you have to just test the rest.

Answer 5

[Aleksandr Levchuk]

Identification of Selective Inhibitors of Cancer Stem Cells

http://www.sciencedirect.com/science/article/pii/S0092867409007818

Identified drug: salinomycin

One compound, salinomycin, reduces the proportion of CSCs by >100-fold relative to paclitaxel, a commonly used breast cancer chemotherapeutic drug.

[Flow]

this is very interesting, but the compound was discovered thanks to a chemical compound screening, so the computer prediction was not used

[Aleksandr Levchuk]

@flow, good point

Answer 6

[Aleksandr Levchuk]

My colleague suggested the following article for this answer:

Lessons learned from the development of an Abl tyrosine kinase inhibitor for chronic myelogenous leukemia http://www.jci.org/articles/view/9083

But they also used random screening to get the initial lead compound.

As is the case with many of the inhibitors currently in clinical trials, an initial lead compound was identified by the time-consuming process of random screening, that is, the testing of large compound libraries for inhibition of protein kinases in vitro.

So this is also not a real answer.

[Flow]

yeah, indeed very interesting but not this kind of paper

Answer 7

[Hitchpy]

I find one of this year's Science Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin
very inspiring,http://www.sciencemag.org/content/332/6031/816.full.html

[Flow]

very interesting, but I am looking for computational design/prediction of small molecules (drugs) and not proteins

[Hitchpy]

http://bib.oxfordjournals.org/content/12/4.toc In this month's Briefing inBioinformatic they seems to talk about this topic