this is the “brainless” part Chris describes—it just gives “an answer.” It does not engender interactions with experimentalists—there is no tradition of the back-and-forth between theory and experiment that drives scientific fields forward. Finally, there is no sense of generalization, although the MD world would say this is nonsense because it is the most general of techniques- Top Listings
I love reading your blog..And I'm amazed with the information that's new for me.
Thanks a lot Anthony.. - Nickets
OK. You're heretical. Fortunately, in this century I think this won't get you burned at the stake!
Here's what Shrodinger is going to do with the money (excerpted from: http://www.schrodinger.com/new...
"First, additional resources will be allocated to existing projects that we view as critical, such as induced fit docking and scoring function development. Next, we will create novel tools for ADME prediction and modeling GPCRs, both of which will require major new research efforts with a long term commitment. Finally, we intend to develop a database of protein-ligand interactions, which will represent a significant advancement over currently available commercial products."
Little described above can actually be described as science unless they are going to do some experiments....
I believe that physics does probably hold the answer. However, in the 'old physics' ways, there was a competition between the experimentalists and the theoreticians. Right now, at least in the "practical" world of drug discovery, the game isn't even close, so the experimentalists generally aren't worried about challenging what the theoreticians say, since A) They lose so infrequently and B) Regardless of the "sophistication" of any model, only the experiment will be used to make a high stake decision. I hope the biotech/drug industry can get better at providing more meaningful data in the future.
Until physics allows us to see 'the answer', demonstrable usefulness is a noble goal. Good Luck! (you have 1770 days left) - CSage
I believe Physics to be the queen of all sciences! - Custom Essay
I couldn't agree more. $10M would buy a lot of useful experiments. SAMPL3 (August 1st 2011) does have some real, live experimental data not yet published- but even this is not the back and forth between experiment and theory that the field really needs. 1768 days to go. - Ant
What's the matter with you people? I *know* you don't (all) agree with me. I really won't hunt you down. Probably. - Ant
I believe a good idea explains what you intended it to.
It is a good idea to get enough rest each night. It explains why we can function well on a daily basis. (of course other factors need be satisfied as well, but getting no sleep for three straight nights will not allow you to function well). Getting enough sleep may *also* explain more beneficial advantages. But even if it doesn't that doesn't mean it's a bad idea.
It's a good idea to eat too. If we don't we will die.
Running MD is often a good idea when I need limited sampling and can trust it to do a good job of say allowing sidechain exploration in the local environment of a protein/ligand complex. I can trust the results it generates more than most of the rotamer library databases which know nothing of the local environment. MD is not necesarily better than MC but it does work well. And so it is a good idea.
Not all good ideas are the best ideas. And not all good ideas have to explain more than they are intended to.
For a given sidechain motion, there can be other better methods for sampling. But as long as MD gives me reasonable results in a matter of 1-2 minutes it is good enough. Limited MD in my project support has often prospectively predicted previously unseen protein loop and sidechain movement, with subsequent corroboration by crystallography. (And even in cases where there has not yet been experimental validation, the ideas are still useful. If you play the lottery and win $1000 for every $100 spent and lose 90% of the time, it's still a good financial decision for you to buy those tickets).
And the same MD has prospectively predicted accurate folding times for small proteins. Is that not generalisable?
What percent of instances does it need to work in for you to call it generalisable?
Generalisability should not mean it has to succeed in every single case.
You've defined your ideology of what a good idea is. And maybe MD does not meet those criteria. I submit that you are too restrictive on what makes an idea (or method) good.