3 ms·

> Sorry, to be clear, there usually isn't any machine learning involved (at least not in the examples I'm familiar with), but the rest of the process is very si

by jf- 8y ago

> Sorry, to be clear, there usually isn't any machine learning involved (at least not in the examples I'm familiar with), but the rest of the process is very similar. No problem, shame though! It seems to me there must be scope for using AI to improve this process given the results it achieves in other domains, and the alphafold result is very encouraging. Maybe that order of magnitude improvement will eventually be possible.

natechols 8y ago
In molecular modeling, at some point you're limited either by quantity or quality of input data (for example known protein structures), by the accuracy of your energy function, or by the inability to simulate at long timescales. AI alone won't magically solve the problem without fixing the others too. (AI combined with quantum computing, maybe?) I am much more excited about the application of AI to more complex problems like metabolic engineering/synthetic biology, literature mining, and genome-wide association studies. It's a shame the training data are such an incomplete mess, but that'll improve slowly.
lambdadmitry 8y ago
To add to the GP, there are at least two more fundamental problems with the computational approach: - chaperones. Not all proteins fold by themselves, quite a few bind to an additional protein that helps them fold in the desired shape. It means that the final state is impossible to achieve from the "initial" state by gradient descent. - proteins don't necessarily exist in the minimum potential energy state. Moreover, sometimes the state flips on addition of a ligand (e.g. myosin's relationship with ATP) and that's crucial for the protein function. So static folding only gets you so far. Unfortunately, nature is hideously complicated and "entangled", so there is a tremendous gap between even perfect protein folding and real in vitro results.

by jf- 8y ago

natechols 8y ago

In molecular modeling, at some point you're limited either by quantity or quality of input data (for example known protein structures), by the accuracy of your energy function, or by the inability to simulate at long timescales. AI alone won't magically solve the problem without fixing the others too. (AI combined with quantum computing, maybe?) I am much more excited about the application of AI to more complex problems like metabolic engineering/synthetic biology, literature mining, and genome-wide association studies. It's a shame the training data are such an incomplete mess, but that'll improve slowly.

lambdadmitry 8y ago

To add to the GP, there are at least two more fundamental problems with the computational approach: - chaperones. Not all proteins fold by themselves, quite a few bind to an additional protein that helps them fold in the desired shape. It means that the final state is impossible to achieve from the "initial" state by gradient descent. - proteins don't necessarily exist in the minimum potential energy state. Moreover, sometimes the state flips on addition of a ligand (e.g. myosin's relationship with ATP) and that's crucial for the protein function. So static folding only gets you so far. Unfortunately, nature is hideously complicated and "entangled", so there is a tremendous gap between even perfect protein folding and real in vitro results.