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The neural network community uses proper "training"/"validation"/"test" datasets to asses model performance, and has developed algorithms to fit complex models
by jostmey 10y ago
The neural network community uses proper "training"/"validation"/"test" datasets to asses model performance, and has developed algorithms to fit complex models to large amounts of data using relatively little computing power. I think it is possible to build completely new and accurate models of biophysical systems with neural networks with relative ease.
Using models like variational autoencoders it might be possible to draw truly independent samples in a single step, instead of relying lots of MD steps to find novel conformations.
I could go on, but I have to get to bed now :-)
- cing 10y agoI like the idea, but I wouldn't call it "relative ease". Are we talking about making better force fields, or training a recurrent neural network for generative dynamics that obeys the laws of physics (can be used to calculate ensemble averages) for arbitrary length proteins/folds? How do you construct a VAE for protein dynamics when you only have a single structure? There's a serious lack of data that prevents these things.