
Deep learning applications in drug discovery and protein structure analysis - msapaydin
https://msapaydin.wordpress.com/2019/02/16/on-protein-representations-for-drug-discovery-with-deep-learning-applications/
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Protostome
Re grid representation - The convolution operator is translation equivariant.
(waving hands, it means that the translation operated on the Input Signal is
still detectable in the output features set)

However, it was shown many times that coupling the convolution operator with a
pooling layer achieves translation invariance by means of dimensionality
reduction.

Moreover, rotational equivariance (and subsequently invariance) is an active
area of research. There's an interesting talk
([https://www.youtube.com/watch?v=-UKL3kOlOds&list=PLlMMtlgw6q...](https://www.youtube.com/watch?v=-UKL3kOlOds&list=PLlMMtlgw6qNjROoMNTBQjAcdx53kV50cS&index=18))
by Boomsma/Frellsen about the use of spherical convolutions in deep learning
applications of molecular structures.

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syntaxing
I wish there was more information in this article. This subject is super
interesting to me but I do not know where to start on the non deep learning
aspect. Does anyone have any pointers?

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abcc8
I'd suggest starting with the references cited in the article and reading the
references cited in those articles, as relevant.

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neltnerb
I really liked these, but they're particular to my interests:
[http://www.brianneltner.com/machine-
learning](http://www.brianneltner.com/machine-learning)

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syntaxing
Your post is super interesting! Thank you for sharing!

