AI can't help much in drug discovery (i.e lead generation) because the pharamecutical industry is suffering from Eroom's law ( https://en.wikipedia.org/wiki/Eroom%27s_law ). It's a play off Moore's law (spelled backwards), but the fundamental problem is the pharma/medical industries do not understand biology from first principles, unlike hard physical sciences, such as computer hardware engineering.
First principles understanding of computing hardware allows chip manufacturers to create novel, new chips from scratch, as we fully understand the physics behind electricity, how logic gates work and signals propagate through physical mediums. Lock most college students in a room with resistors, diodes, wire, etc, and reference material, and they could recreate basic circuitry and very simple computers. You can not do the same with biology - no student or expert, given unlimited resources, equipment, or reference material can construct a new living cell from scratch (proteins / molecules). This is not a slander of those sciences and scientists, but there is simply a huge gap in how well we understand the basic principles of life and how well we understand the basic principles of computing.
Throwing more resources / computing into "drug discovery" is like trying to build chips by wiring computer parts differently. Occasionally it might work and produce a "useful" result, but it's fundamentally a broken approach.
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.
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.]
>You can not do the same with biology - no student or expert, given unlimited resources, equipment, or reference material can construct a new living cell from scratch (proteins / molecules).
The last paragraph of that article proves the OP's point, you can't do it by working from first principles.
"Even Venter acknowledges that syn3.0’s genome, although new, was designed by trial and error, rather than being based on a fundamental understanding of how to build a functioning genome."
This is why I was never good at biology and hated both classes the classes I took in it. Coming from a CS background, it was so frustrating not to be able to reason from first principles about the nature of our observations.
First principles understanding of computing hardware allows chip manufacturers to create novel, new chips from scratch, as we fully understand the physics behind electricity, how logic gates work and signals propagate through physical mediums. Lock most college students in a room with resistors, diodes, wire, etc, and reference material, and they could recreate basic circuitry and very simple computers. You can not do the same with biology - no student or expert, given unlimited resources, equipment, or reference material can construct a new living cell from scratch (proteins / molecules). This is not a slander of those sciences and scientists, but there is simply a huge gap in how well we understand the basic principles of life and how well we understand the basic principles of computing.
Throwing more resources / computing into "drug discovery" is like trying to build chips by wiring computer parts differently. Occasionally it might work and produce a "useful" result, but it's fundamentally a broken approach.