This is pretty clear proof that top-tier ML engineers can outperform pharma teams at problems relevant to drug discovery.https://twitter.com/AndrewCutler13/status/1194706278074871809 …
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Sarah Constantin Retweeted Andrew Cutler
This is pretty clear proof that top-tier ML engineers can outperform pharma teams at problems relevant to drug discovery.https://twitter.com/AndrewCutler13/status/1194706278074871809 …
Sarah Constantin added,
If you click through to the blog post, it’s also clear that computational protein structure prediction basically “doesn’t work” currently, in the sense of being able to predict which drugs would bind to a protein, well enough to replace experiments. 40% accuracy? Ick.
Today’s machine learning isn’t enough to solve the problem, but it isn’t window dressing either. Accuracy in protein structure prediction has reliably improved as neural network architectures have gotten deeper and more powerful.
Sarah, while the Google team's results were impressive, I think their better numbers came from not actually trying to solve the same problem. Think about it: instead of predicting binding based on sequences, they're predicting distances between sequence pairs.
And the model is trained on a large set of known protein structures&sequences. Now, why would a pair of sequences want to be at a specific long distance from each other? They don't; that correlation is a result of evolutionary similarity!
But as you noted, current models aren't good enough. And models trained to guess structure by assuming distances between sequence pairs match what's in the existing library are fundamentally limited. The other teams got worse results because their assumptions are more physical!
Ah! That is damning if true.
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