Could you share the critical feedback you gave? I am interested as someone who works with biological systems and is curious about how ML can or cannot help.
I told him that: the increased speed but lower accuracy of their protein structure predictor was not useful because the only thing that matters in PSP is absolute prediction quality. And that speeding up that step wouldn't have any impact on pharmaceutical development, which is one of his claims (closed-loop protein design).
Without being an expert in this matter, this seems wrong.
Sure you want quality here but there’s always going to be a human in the loop for this kind of work.
Any workflow with a human in the loop has this speed vs accuracy tradeoff.
While I’m not saying that speed trumps accuracy here, I don’t think you can dismiss without evidence that the tradeoff exists and speed might have benefits.
Lab work and clinical trials are incredibly slow. A single experiment testing a single candidate might take weeks (in cell lines), months (rodents) or even years (humans/non-human primates). You're going to do a bunch of them and they often require expensive reagents and/or tedious work.
Consequently, shortening the wait for a predicted structure by a few hours (or days) won't really move the needle. This is especially true if it makes your experiment, already probably a long shot, less likely to succeed.
GP is saying that the slow part of pharmaceutical development (synthesis,trials,etc) takes so many orders of magnitude longer than the software part (candidate generation) that any speed improvement is moot. In fact having lower quality software generated candidates merely leads to wasted time later.
