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So text space is trivially vectorizable, at the character level and even for difficult languages like Russian chunk-vectorisable with some care. How do you enco

So text space is trivially vectorizable, at the character level and even for difficult languages like Russian chunk-vectorisable with some care. How do you encode the difference between houamine A and atrop-houamine A, while keeping the similarities, without resorting to empirical measurements and classification, which could yield reasonable vectors, but will take 2-5 years of a highly trained grad student's labor to obtain and put into the training corpus

deleted 5y ago
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timr 5y ago
> How do you encode the difference between houamine A and atrop-houamine A, There are now lots of ways of encoding molecules. So many, in fact, that it's not really worth debating the merits of any particular method. ECFP fingerprints shoved into a fully connected NN work surprisingly well for a large class of problems. Molecular graph convolutions (of which there are now many flavors) also work well. The field is to the point where people are doing ensembles of different encodings, and seeing what works for any particular problem. > without resorting to empirical measurements and classification, which could yield reasonable vectors, but will take 2-5 years of a highly trained grad student's labor to obtain and put into the training corpus Well, you're sort of touching on my last paragraph with this. The classifier, featurization, etc., usually matters less (a lot less?) than the quality of the assay data. So I agree in that respect.
- dnautics 5y ago
  > Molecular graph convolutions "also work well" What is the metric for "working well" here? My point is a graph convolution will have a hard time distinguishing the haouamine atropisomers, because they have the same graph, but very different activities. This sort of weird shit is the norm. Like some molecule that isn't the actual active form but needs cyp450 to be activated. But it won't be if you add a methyl group that reduces it toxicity, because that methyl blocks cyp (not your target even). But only in college student pre-phase I volunteers, who happen to be predominantly white male college students looking for beer money In the end your corpus is going to have to fall back to experiment, which is just so much slower in chemistry than the trivially parallel "scrape every Wikipedia article, twitter tweet, and reddit comment" that you have access to for text corpora; it's more like trying to use ml to decode Chinese, if we only had 3000 Chinese documents to work with.
  - timr 5y ago
    > What is the metric for "working well" here? Model fit. Validation on a holdout set. All of the usual standards for ML performance. > My point is a graph convolution will have a hard time distinguishing the haouamine atropisomers, because they have the same graph, but very different activities. Isomers can be represented. The typical problem with isomers is that you don't have good data on them, because real-world laboratory solutions are rarely pure. > Like some molecule that isn't the actual active form but needs cyp450 to be activated. Drug metabolism is a common set of endpoints for ML models. Nothing you're describing is so mysterious or difficult that the problems "break" machine learning in this domain. > In the end your corpus is going to have to fall back to experiment All ML models in this space depend on experiment.
rsfern 5y ago
There are a ton of interesting graph neural network architectures that have been developed over the last 5-6 years, they are getting pretty good at modeling the properties of molecules and crystals Edit: saw your other comment on GNNs. I don’t think it’s like a solved problem or anything, but I also don’t think it’s intractable. e.g. https://arxiv.org/abs/2012.00094 https://arxiv.org/abs/2012.00094 seems relevant

deleted 5y ago

[deleted]

timr 5y ago

> How do you encode the difference between houamine A and atrop-houamine A, There are now lots of ways of encoding molecules. So many, in fact, that it's not really worth debating the merits of any particular method. ECFP fingerprints shoved into a fully connected NN work surprisingly well for a large class of problems. Molecular graph convolutions (of which there are now many flavors) also work well. The field is to the point where people are doing ensembles of different encodings, and seeing what works for any particular problem. > without resorting to empirical measurements and classification, which could yield reasonable vectors, but will take 2-5 years of a highly trained grad student's labor to obtain and put into the training corpus Well, you're sort of touching on my last paragraph with this. The classifier, featurization, etc., usually matters less (a lot less?) than the quality of the assay data. So I agree in that respect.

dnautics 5y ago
> Molecular graph convolutions "also work well" What is the metric for "working well" here? My point is a graph convolution will have a hard time distinguishing the haouamine atropisomers, because they have the same graph, but very different activities. This sort of weird shit is the norm. Like some molecule that isn't the actual active form but needs cyp450 to be activated. But it won't be if you add a methyl group that reduces it toxicity, because that methyl blocks cyp (not your target even). But only in college student pre-phase I volunteers, who happen to be predominantly white male college students looking for beer money In the end your corpus is going to have to fall back to experiment, which is just so much slower in chemistry than the trivially parallel "scrape every Wikipedia article, twitter tweet, and reddit comment" that you have access to for text corpora; it's more like trying to use ml to decode Chinese, if we only had 3000 Chinese documents to work with.
- timr 5y ago
  > What is the metric for "working well" here? Model fit. Validation on a holdout set. All of the usual standards for ML performance. > My point is a graph convolution will have a hard time distinguishing the haouamine atropisomers, because they have the same graph, but very different activities. Isomers can be represented. The typical problem with isomers is that you don't have good data on them, because real-world laboratory solutions are rarely pure. > Like some molecule that isn't the actual active form but needs cyp450 to be activated. Drug metabolism is a common set of endpoints for ML models. Nothing you're describing is so mysterious or difficult that the problems "break" machine learning in this domain. > In the end your corpus is going to have to fall back to experiment All ML models in this space depend on experiment.

rsfern 5y ago

There are a ton of interesting graph neural network architectures that have been developed over the last 5-6 years, they are getting pretty good at modeling the properties of molecules and crystals Edit: saw your other comment on GNNs. I don’t think it’s like a solved problem or anything, but I also don’t think it’s intractable. e.g. https://arxiv.org/abs/2012.00094 https://arxiv.org/abs/2012.00094 seems relevant