12 ms·

MD/PhD student in computational biology: What kind of information actually goes into your neural net to predict binding affinity? How does your model actually

by et2o 12y ago

MD/PhD student in computational biology: What kind of information actually goes into your neural net to predict binding affinity? How does your model actually work? I've some experience with MD simulations; even these, which have had 1000s of man-hours of parameterization, are often not very accurate. I'm curious what you are using to evaluate your model's predictions. How are you deciding what your targets actually are? How do you relate a particular drug target to a disease in the case of complex phenotypes? This seems to me to be by far the most challenging part of pharmacology. I'm definitely enthusiastic about applying computation to biology, but as someone also on the frontlines, it's definitely not a clean analogy to computation in engineering. Biological systems are far more complex, interconnected, and nonlinear which is something that is sometimes underestimated.

dluan 12y ago
Sometimes instead of serializing the man hours, it's more effective to parallelize them (see FoldIt, GalaxyZoo, etc). I got to work in the Baker Lab that developed this: http://www.ncbi.nlm.nih.gov/pubmed/22267011 http://www.ncbi.nlm.nih.gov/pubmed/22267011
- et2o 12y ago
  I'm definitely familiar with foldit. I TA'd a class where we have extra credit to students for playing around with it. It's very cool! Thanks for your work.
aheifets 12y ago
Great questions! The typical input to the neural network is the 3D structure of the molecule and of the protein. The model works by detecting patterns in the pair of protein and the drug that correlate with binding, e.g. hydrogen bonding, halogen bonding, cation-pi, pi-pi interactions, etc. But these are complicated to encode manually, given all of the factors that affect binding strength: distance, angle, water mediated effects, resonance, (de)stabilizing environmental charges, etc. That's why we need the neural net: you can think of it as the network automatically deriving the best pharmacophoric features to maximally explain which training examples bind and which ones don't, and then the prediction step is looking for the presence or absence of those patterns in new protein-ligand pairs. We evaluate our models both retrospectively and prospectively. For example, the DUD-E benchmark (http://dude.docking.org/ http://dude.docking.org/) gives us an assessment of our performance over more than a million individual predictions, comprising many diseases and many biological classes (GPCR, nuclear receptor, enzyme, etc). It begins with 102 disease proteins and, for each one, has a set of molecules that bind to the protein and a set that don't. We shuffle those sets together and ask the neural net to "pick the aces out of the deck". Separately, we perform prospective evaluations, for settings where no one knows the right answer, and run the experiment to confirm the predictions. I agree with you that the proper selection of targets is critical, as is the mapping between drug target and disease. For us, however, this is easy: we work with smart biologists! If you have any, please send them our way! Finally, I agree with your point that biology is not designed to be understood by people. That said, molecular binding is fundamental enough that we could think of it as an example of physics rather than biology. And theory works so well for physics that, in many a physics lab, if an experiment disagrees with theory then the first step is to double-check the experiment for errors. The trick is to scale that up to larger systems. Semi-relevant: http://www.smbc-comics.com/?id=2272 http://www.smbc-comics.com/?id=2272

by et2o 12y ago

dluan 12y ago

Sometimes instead of serializing the man hours, it's more effective to parallelize them (see FoldIt, GalaxyZoo, etc). I got to work in the Baker Lab that developed this: http://www.ncbi.nlm.nih.gov/pubmed/22267011 http://www.ncbi.nlm.nih.gov/pubmed/22267011

et2o 12y ago
I'm definitely familiar with foldit. I TA'd a class where we have extra credit to students for playing around with it. It's very cool! Thanks for your work.

aheifets 12y ago

Great questions! The typical input to the neural network is the 3D structure of the molecule and of the protein. The model works by detecting patterns in the pair of protein and the drug that correlate with binding, e.g. hydrogen bonding, halogen bonding, cation-pi, pi-pi interactions, etc. But these are complicated to encode manually, given all of the factors that affect binding strength: distance, angle, water mediated effects, resonance, (de)stabilizing environmental charges, etc. That's why we need the neural net: you can think of it as the network automatically deriving the best pharmacophoric features to maximally explain which training examples bind and which ones don't, and then the prediction step is looking for the presence or absence of those patterns in new protein-ligand pairs. We evaluate our models both retrospectively and prospectively. For example, the DUD-E benchmark (http://dude.docking.org/ http://dude.docking.org/) gives us an assessment of our performance over more than a million individual predictions, comprising many diseases and many biological classes (GPCR, nuclear receptor, enzyme, etc). It begins with 102 disease proteins and, for each one, has a set of molecules that bind to the protein and a set that don't. We shuffle those sets together and ask the neural net to "pick the aces out of the deck". Separately, we perform prospective evaluations, for settings where no one knows the right answer, and run the experiment to confirm the predictions. I agree with you that the proper selection of targets is critical, as is the mapping between drug target and disease. For us, however, this is easy: we work with smart biologists! If you have any, please send them our way! Finally, I agree with your point that biology is not designed to be understood by people. That said, molecular binding is fundamental enough that we could think of it as an example of physics rather than biology. And theory works so well for physics that, in many a physics lab, if an experiment disagrees with theory then the first step is to double-check the experiment for errors. The trick is to scale that up to larger systems. Semi-relevant: http://www.smbc-comics.com/?id=2272 http://www.smbc-comics.com/?id=2272