> Going with your analogy, this would be like training a net using a database of lock structures and their corresponding keys, and then having it predict a key

> Going with your analogy, this would be like training a net using a database of lock structures and their corresponding keys, and then having it predict a key for a given lock. If we have that, then the problem is entirely trivial- just look at the lengths of the tumblers, and give the key teeth that match. And so it is, with the corresponding increase in complexity, in biology- given a complete and confident structure of the target, we have (non-AI, reasonably reliable) chemical modelling methods to see whether and how a given compound will bind; iterate molecules until you find one that works and you've found your "key" quite cheaply. So, there's another limitation to the practicality of AI- when it's not irreducibly complex and intrinsically unknown, biology is usually mechanistic and deterministic enough that conventional approaches can reason about it quite effectively without resorting to anything so fancy as a neural network. Biology is almost always more complicated than a given description, so yes, the real problem is more complicated. As you mention, the key we create must not only fit the target lock, but it must also not fit any other locks that might cause problems. Perhaps AI methods could try to make a working key that is un-key-like as possible, to make it unlikely to fit off-target locks; potentially useful. But, absent knowledge of the various perverse configurations that locks in the wider world can have, the confidence the AI method's inferences are very limited- roughly speaking, it's attempting to make a classifier for a population that is mostly unknown. We might hope for a severalfold increase in the number of successful drug candidates, in the best case, but not a fundamental disruption of the pharmaceutical research process.