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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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.
A meta point: The practical question to ask of any ML algorithm is “what expensive manual process can this replace”? In biotech, that will usually mean “predict the result of an experiment so well you don’t have to run it” or “classify sample data so a human doesn’t have to”
Initial binding-affinity experiments are automated and scalable enough today that any computational chemistry algorithm would have to pass a *really* high bar for it to usefully replace these experiments.
how much better did deepmind manage to do?
10 percentage points better than the next best team; 40% vs 30%
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