
A Graph Convolutional Neural Network Approach to Antibiotic Discovery - wickwavy
https://www.welcometothejungle.com/en/articles/btc-covid19-convolutional-neural-network
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GenesisTx
This post is nicely written. We are from the Pande Lab @ Stanford and are very
supportive of this work being done by Barzilay et al at MIT. We cite each
others' papers for good reason.

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nicklo
Graph convolutions are really powerful for handing structured data like
chemical compositions. With the right corpus, I think this area is ripe for
unsupervised feature representation learning approaches like what we've seen
with BERT-like approaches and how they've dominated NLP in the past few years.

Side note: I worked with Kyle a few years ago on the MIT-MGH Deep Learning for
Mammography project. I'm glad to see his work + brilliance being recognized.

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ptrenko
Unrelated question for the AI experts here: What's the potential for AutoML
solving problems with just X and Y. I understand that you'd need to search a
large subspace but at what stage is it currently and can it solve problems as
complex as the one here?

~~~
nil-sec
If with AutoML you mean substantial architecture search (as your question
seems to suggest) then the answer is it’s feasible only for the largest
players in the field. It’s possible to use some tricks but even then your
standard cluster with a few hundred GPUs isn’t going to cut it for large scale
problems. That said, any problem that e.g. google deems valuable enough can be
meta-optimized to oblivion. Same holds if you have a huge pile of cash and
think a few percent performance boost is worth it.

~~~
ptrenko
So AutoML is not smart yet? Its just about throwing more compute?

~~~
nil-sec
Nothing is smart yet. What people understand under automl is different but I’m
referring to meta learning here, be it the architecture, the optimizer, the
learning rate etc. Optimizing these things requires an outer loop in addition
to training the architecture and for architecture search in particular this
boils down to training lots of models and evaluating all of them. There are
ways of making this search a bit better than random search with techniques
such as regularized evolution (essentially evolution with a bias for younger
individuals) or other tricks but all of them require huge compute resources.

