
Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry - wei_jok
https://preferredresearch.jp/2017/12/18/chainer-chemistry-beta-release/
======
lilleswing
Really cool stuff here, a lot along the same lines of what we have been trying
to do with DeepChem and MoleculeNet.

[https://deepchem.io/](https://deepchem.io/)

[https://github.com/deepchem/deepchem](https://github.com/deepchem/deepchem)

[http://moleculenet.ai/](http://moleculenet.ai/)

We seem to be a little further along and stable than this repo. We are also
based on Tensorflow instead of raw cuda which means users can play around with
the networks more easily.

~~~
akapajama
This is based on Chainer, not raw cuda, meaning its also very flexible for the
user

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brilee
Excellent. I'd been wanting to implement something like this but these guys
beat me to the punch.

Most existing research on NNs in chemistry uses the SMILES string
representation and feeds it into a recurrent neural network. The problem with
SMILES in my opinion, is that SMILES is not unique, and there are a gazillion
ways to represent the same molecule. Thus, the RNN is probably overfitting to
the quirks of the SMILES serialization algorithm used by the US Patent Office
and/or ChemDraw and/or whatever program was used to generate the library of
molecules.

Convolutional graph networks are the obviously correct way to handle
molecules.

~~~
dekhn
they use canonical SMILES. Problem: there's more than one way to make
canonical SMILES :(

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Pulcinella
This seems like the development of an artificial chemical intuition[0][1]
except since its a computer it can intuit concrete numbers much more
comfortably than humans can. I do wonder how its accuracy compares to just
running the DFT calculations.

[0]wavefunction.fieldofscience.com/2016/09/what-is-chemical-intuition.html

[1][https://news.ycombinator.com/item?id=12624036](https://news.ycombinator.com/item?id=12624036)

~~~
lilleswing
There is a lot of research on attempting to approximate DFT level energy
calculations with simple Neural Networks.

[https://arxiv.org/abs/1610.08935](https://arxiv.org/abs/1610.08935)
[https://github.com/deepchem/deepchem/blob/master/deepchem/mo...](https://github.com/deepchem/deepchem/blob/master/deepchem/models/tensorgraph/models/symmetry_function_regression.py#L113)

[http://aip.scitation.org/doi/pdf/10.1063/1.4986081](http://aip.scitation.org/doi/pdf/10.1063/1.4986081)

It is a pretty straight-forward application of ML to the space that is
starting to have solid results in practice.

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corochann
Links of Chainer Chemistry

\- blog: [https://preferredresearch.jp/2017/12/18/chainer-chemistry-
be...](https://preferredresearch.jp/2017/12/18/chainer-chemistry-beta-
release/)

\- github: [https://github.com/pfnet-research/chainer-
chemistry](https://github.com/pfnet-research/chainer-chemistry)

\- document: [http://chainer-
chemistry.readthedocs.io/en/latest/index.html](http://chainer-
chemistry.readthedocs.io/en/latest/index.html)

