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This is the future of computational chemistry. The field of "Molecular Dynamics" could easily be swept aside by machine learning. It is why I switched from runn

by jostmey 10y ago

This is the future of computational chemistry. The field of "Molecular Dynamics" could easily be swept aside by machine learning. It is why I switched from running "Molecular Dynamic" simulations as a graduate student to modelling genomic data with TensorFlow as a Postdoc.

dkural 10y ago
What kind of genomic data out of curiosity?
- dnautics 10y ago
  Is that actually working? I tried rolling my own neural nets and running genomic data through them and instantly realized the problem was low N, high autocorrelation in the data. Bayesian forests seem like a better choice, but that's me.
  - deleted 10y ago
    [deleted]
  - alextheparrot 10y ago
    There have been some recent papers in subsets of genomics with a bit more data (Transcription factor binding, for example [1]). You're correct though, definitely depends on your problem. [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908339/pdf/btw255.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908339/pdf/btw...
cing 10y ago
How do you suppose the questions you've answered working on ion channels would be addressed with machine learning? I get the impression by "swept aside" that you feel the field of molecular biophysics is not meaningful in the study of health and medicine.
- 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 ago
    I 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.
- momeara 10y ago
  Molecular dynamics simulations can be used to answer a range of structural biology questions, but abstractly many of them can be phrased as evaluating the difference in free energy between different conformational states. In molecular dynamics this is done by thermodynamic integrating the energy of over the state space volume for each of the conformational states. An alternative approach is to directly map conformational states to their free energy. This leads to a problem of searching for candidate conformational states (e.g. the folded state, transition states etc.) and scoring them. Usually for a given computational budget there is a trade off between better conformational sampling or higher accuracy energy scoring. Historically, searching and scoring methods have been designed separately. For example [1] improves sampling while [2] improves energetics. This is done because they historically involved different aspects of the simulation and each is lot of work. But searching and sampling are not really separable, in that the deeper one samples the more challenging the task of the scoring function becomes--discriminating stable from unstable conformations. Another application that can be thought of as searching and scoring is the game of GO. My impression is that one of the major breakthroughs with AlphaGo is that they were able to integrate models for searching and scoring together and learn the models simultaneously. It would be awesome if similar architectures could be applied to molecular modeling. A remaining challenge in applying GO models to molecular biology is that while the representation and scoring rules for GO are fixed and quite easy, the ground truth for molecular simulations comes from heterogenous experimental data (X-ray crystal structures, small molecule activities, directed evolution antibody screens etc.) and higher levels of theory QM simulations, which have their own challenges. However, I think the principles carry over--complicated scoring functions (e.g. free energy) over large state spaces (e.g. protein conformation space or chemical space) can be learned by combining models for searching and scoring. I think deep learning is poised to tackle these problems. [1] (Conway, et al., 2013, DOI: 10.1002/pro.2389) Relaxation of backbone bond geometry improves protein energy landscape modeling [2] (Park, 2016, PMID: 27766851) Simultaneous optimization of biomolecular energy function on features from small molecules and macromolecules.
zump 10y ago
So DE Shaw Research is barking up the wrong tree?
rgbombarelli 10y ago
Who says you cannot do deep learning and molecular dynamics? There are a couple of recent examples of neural networks to predicted energies and gradients allowing faster computation and larger time steps in MD simulations. https://arxiv.org/abs/1609.08259 https://arxiv.org/abs/1609.08259
Xcelerate 10y ago
I agree. I did molecular dynamics simulations for my PhD. The last year I spent some time exploring feature representations that capture atomic environments up to their symmetry invariants (rotations, reflections, permutations of identical atoms, etc.) The use of machine learning for MD and quantum chemistry has exploded in the last year and a half. I'm a bit sad that I just finished my degree right as this kind of work is heating up.
- pizza 10y ago
  h-suppressed graphs? VESPR graph stuff? compressive sampling / measuring of typical states? ligand complices? ligand transport? membrane dynamics? predictive toxicology? cheaper hartree-fock approximations? quark shit??? time to daydream
pizza 10y ago
Please, feed me more specifics while I salivate. In as well-defined a problem/mission statement/objective as possible, what are you doing (and with what / how), and where do you intend to reach?

by jostmey 10y ago

dkural 10y ago

What kind of genomic data out of curiosity?

dnautics 10y ago
Is that actually working? I tried rolling my own neural nets and running genomic data through them and instantly realized the problem was low N, high autocorrelation in the data. Bayesian forests seem like a better choice, but that's me.
- deleted 10y ago
  [deleted]
- alextheparrot 10y ago
  There have been some recent papers in subsets of genomics with a bit more data (Transcription factor binding, for example [1]). You're correct though, definitely depends on your problem. [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908339/pdf/btw255.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908339/pdf/btw...

cing 10y ago

How do you suppose the questions you've answered working on ion channels would be addressed with machine learning? I get the impression by "swept aside" that you feel the field of molecular biophysics is not meaningful in the study of health and medicine.

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 ago
  I 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.
momeara 10y ago
Molecular dynamics simulations can be used to answer a range of structural biology questions, but abstractly many of them can be phrased as evaluating the difference in free energy between different conformational states. In molecular dynamics this is done by thermodynamic integrating the energy of over the state space volume for each of the conformational states. An alternative approach is to directly map conformational states to their free energy. This leads to a problem of searching for candidate conformational states (e.g. the folded state, transition states etc.) and scoring them. Usually for a given computational budget there is a trade off between better conformational sampling or higher accuracy energy scoring. Historically, searching and scoring methods have been designed separately. For example [1] improves sampling while [2] improves energetics. This is done because they historically involved different aspects of the simulation and each is lot of work. But searching and sampling are not really separable, in that the deeper one samples the more challenging the task of the scoring function becomes--discriminating stable from unstable conformations. Another application that can be thought of as searching and scoring is the game of GO. My impression is that one of the major breakthroughs with AlphaGo is that they were able to integrate models for searching and scoring together and learn the models simultaneously. It would be awesome if similar architectures could be applied to molecular modeling. A remaining challenge in applying GO models to molecular biology is that while the representation and scoring rules for GO are fixed and quite easy, the ground truth for molecular simulations comes from heterogenous experimental data (X-ray crystal structures, small molecule activities, directed evolution antibody screens etc.) and higher levels of theory QM simulations, which have their own challenges. However, I think the principles carry over--complicated scoring functions (e.g. free energy) over large state spaces (e.g. protein conformation space or chemical space) can be learned by combining models for searching and scoring. I think deep learning is poised to tackle these problems. [1] (Conway, et al., 2013, DOI: 10.1002/pro.2389) Relaxation of backbone bond geometry improves protein energy landscape modeling [2] (Park, 2016, PMID: 27766851) Simultaneous optimization of biomolecular energy function on features from small molecules and macromolecules.

zump 10y ago

So DE Shaw Research is barking up the wrong tree?

rgbombarelli 10y ago

Who says you cannot do deep learning and molecular dynamics? There are a couple of recent examples of neural networks to predicted energies and gradients allowing faster computation and larger time steps in MD simulations. https://arxiv.org/abs/1609.08259 https://arxiv.org/abs/1609.08259

Xcelerate 10y ago

I agree. I did molecular dynamics simulations for my PhD. The last year I spent some time exploring feature representations that capture atomic environments up to their symmetry invariants (rotations, reflections, permutations of identical atoms, etc.) The use of machine learning for MD and quantum chemistry has exploded in the last year and a half. I'm a bit sad that I just finished my degree right as this kind of work is heating up.

pizza 10y ago
h-suppressed graphs? VESPR graph stuff? compressive sampling / measuring of typical states? ligand complices? ligand transport? membrane dynamics? predictive toxicology? cheaper hartree-fock approximations? quark shit??? time to daydream

pizza 10y ago

Please, feed me more specifics while I salivate. In as well-defined a problem/mission statement/objective as possible, what are you doing (and with what / how), and where do you intend to reach?