I was involved in building an automated drug research lab a few years ago. Part of that was having in-silico simulation of chemical structures. Obviously there

I was involved in building an automated drug research lab a few years ago. Part of that was having in-silico simulation of chemical structures. Obviously there was quite a bit of "oh that means we can rapidly return results to customers without incurring the overhead of actually running the tests right? Great for our bottom line" The answer was obviously no, at some point we still need to run actual tests. Without that we just have a proposition of what might happen. And the longer we leave running actual tests to feed back into the models the more "out of sync" with reality we risk becoming. I think thats key to where this article just misses the real world situation. We're heading towards an environment where we can rapidly make propositions of outcomes for a hypothesis based on what we already know to help remove low % avenues and focus in on opportunities. Hypothesise and test still need to happen, but hopefully the predict phase will mean researchers hit dead-ends less often and earlier and can move to other avenues quicker and with fewer resources wasted.