'Machine Learning for Proteins' by Lucy Colwell

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*** This talk is part of IACS's 2019 symposium on the Future of Computation: "Data Science at the Frontier of Discovery: Machine Learning in the Physical World" ***

Presenter: Lucy Colwell, Department of Chemistry, University of Cambridge

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would be interesting to see this work be performed with some of the more new-new (and some not so new-new) neural network architectures and dynamics (i.e. attention, transformers, GANs, selfplay/meta-learning/policy distillation) + combine it with deep learning models for ab-initio physics -->

so you can have faster resolution of molecular dynamics + have a richer encoding space/architecture to soak up or learn the various heuristics that govern:

translating a sequence
+ knowledge of its atomic structure (this is where the deep nets that are trained on QM will help) ---> into a fully resolved 3D structure

also wondering if it is possible to have the "not fully optimally predicted structures" that got spit out by the deepNet and have them "jiggle out" or "anneal" in silico --> basically plug that structure into a larger simulating environment and see of that structure truly is at its lowest energy state, OR is not, OR has several, AND/OR to observe the dynamics of that "predicted" protein

tigeruby

Is the paper for the 'finding new sequences' part available?

rohitgavirni

Plus any one tell...where can I get this slides

sibasankar

Oh -I've seen this kind of thing somewhere before .. but that was another country

skramarobed

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