A watershed moment for protein structure prediction
I am not an ML engineer (except when I program in ML, of course ;)). But this sounds a lot like the following:<p>1. We had a model that worked in principle, but the search space was practically infeasible.<p>2. We made an observation that a different model might exist that makes the search space irrelevant.<p>3. We threw ML at it.<p>4. Now we <i>might</i> have a model that fulfills (2) but we cannot be sure because we used a black-box approach.<p>5. Somehow the results are exciting. Better results would be <i>really</i> exciting.<p>6. We hope that more data yields these better results.<p>Is that correct? Am I the only one to lament these black-box approaches? Should there not be a bunch of people now studying the learned models to figure out if much better results can actually exist?