Comments (8)
from molecule-autoencoder.
Great suggestion. Yes, SMILES is clearly suboptimal for this reason. The molecular autoencoder would almost certainly work better if we used a modified language that had fewer opportunities to produce invalid strings.
from molecule-autoencoder.
Hi, I tried to find the code of bayesian optimization used in this paper. But it seems the code not included. Will you plan to share the code of bo?
from molecule-autoencoder.
I tried use the bayesian optimization to find the better molecules. But when use BO search in the 292 space, I alwasy got invalid smiles same like Hsiao Yi got , so I guess this might be caused by the way to chose inducing point , right?
from molecule-autoencoder.
You were doing BayesOpt in a 292-dimensional space? We were already having a hard time with a 56D space. One thing you might want to look at are the lengthscales of each dimension - we found that they were often very long, and that the GP was basically just doing linear regression.
from molecule-autoencoder.
I will try to upload the code for Bayesian optimization by next week. In our experiments we obtained a large number of invalid smiles. At each point, we decoded a large number of smiles (500) and from those, we only kept the valid ones.
from molecule-autoencoder.
Thank you very much for answering my questions. Yes, I tried 292 dimensions by using GpyOpt. For the lengthscale of each dimension, I use [-1,1], I guess this lengthscale might not be correct. I look forward to your BO code.
from molecule-autoencoder.
Can you suggest why we are using 292 space . What's the logic behind it ?
from molecule-autoencoder.
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from molecule-autoencoder.