[efangs]

YES. Exactly this.

It's fine to make approximations to avoid exponential scaling, but applying function approximators essentially randomly won't get you anywhere. This is then compounded by the fact that the functional framework you're starting from is not a first-principles approach.

Until there is QMC for drug discovery, it will all be hype.

[randcraw]

Mostly hype. Yes, automating drug discovery to any extent is utterly hopeless, and as likely to impact the pharma business any time as autonomous killer robots overrunning the battlefield -- Not In My Lifetime.

But AI definitely has a near term future in addressing well formed questions like specific assays or searching for well-constrained targets, like ligand matches. The trick is for the AI contributor TO LEARN SOMETHING ABOUT THE DAMNED DOMAIN. Unless the chemist/biologist is intimately involved in the task, the AI provider is shooting blind. But with many wise eyes on the ball, even the hardest problems becomes a lot more assailable.

[I say this as someone who processes images and analyzes data within a big pharma, and has seen several grand IT plans fail (like systems biology disease modeling) and many small & specific scientist-assistance tasks succeed.]