Encoding representations

[hkmztrk]

Hello,

I'm pretty new to autoencoders and I know we can use utilize them for unsupervised learning. Is it possible to use this model to create representations (with encoding) for a set of SMILES?

If so, I guess first I had to preprocess my data set, then use sample.py ?

Thanks!

[pechersky]

That's exactly right. Have you taken a look at the Gomez-Bombarelli paper linked in the README?

On Wed, May 17, 2017 at 6:54 AM, hkmztrk [email protected] wrote:

Hello,

I'm pretty new to autoencoders and I know we can use utilize them for unsupervised learning. Is it possible to use this model to create representations (with encoding) for a set of SMILES?

If so, I guess first I had to preprocess my data set, then usesample.py ?

Thanks!

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[hkmztrk]

Yes, it was that paper led me to here. Thanks!

[liamnaka]

Last time I checked the VAE encodings were pretty substantial in size, so if you're trying to learn from a large set of SMILES it might be more feasible to generate them on the fly.

[hkmztrk]

Hello, thanks for your suggestion. Sorry for my asking but, how do we generate them on the fly? Aren't we supposed to learn the model first? How do we do that?

[pechersky]

Those reasons are why we developed sample_gen.py and train_gen.py, which generate the one-hot representations on the fly and can train the model using the fit_generator functionality of Keras.

To train the model in such a way, run python train_gen.py structures.h5 model.h5, where structures.h5 is an h5 file containing structures under the 'structure' key. Check out the source for train_gen.py for more details.

On Thu, May 25, 2017 at 3:01 AM, hkmztrk [email protected] wrote:

Hello, thanks for your suggestion. Sorry for my asking but, how do we generate them on the fly? Aren't we supposed to learn the model first? How do we do that?

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