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> 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 distinguishi

> 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.

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.