I’m not sure why you consider it a great deal of waste.
Building better models that simulate a human biological process must have some sort of payoff? It increases human knowledge, and should provide a foundation on which others can build.
I am highly supportive of DE Shaw's efforts to make the world better. On the other hand, OP has a point: many of these sorts of things actually do not have a payoff. I knew some guys working on protein folding in the 90s. No payoff. One of 'em must be close to retiring by now. Shaw offered me a job to do an innovative kind of FTIR back when I graduated in 2004. Pretty sure that didn't pay off either. For that matter Shaw's original research was into the 'transputer' which also didn't pay off.
The man's a risk taker, and perfectly entitled to spend his post tax income on whatever he likes. But a lot of science and stuff is dead ends, and, of course, avoiding known dead ends. Might very well be a dead end. He's been dumping considerable resources into this project since 2001; that's rather a long time ago now.
> Building better models that simulate a human biological process must have some sort of payoff? It increases human knowledge, and should provide a foundation on which others can build.
That really is still not clear for the models described in the article. In reality, these models are rehashes of what Murcko was having people do 25 years ago. Big articles were written about Vertex applying Free Energy Perturbation with pictures of Murcko accompanied by David Pearlman and Govinda Bhisetti. A book was written about these efforts, The Billion-Dollar Molecule, and more recently a sequel which partially describes how Vertex's efforts using these methods failed (The Antidote).
Obviously, computational power has improved by orders of magnitude since 1989. So have our parameters for modeling proteins and small molecules. But it really is still not clear that MD or FEP really provide any useful insights into proteins that cannot be obtained more simply via NMR-based screens and linear regression. In fact, I recently saw a talk by Relay's VP of Computation where he described using Free Wilson Analysis at Relay[1] for their drug discovery, which is a linear regression method from 1964...
Great context. You don't have to go too far back in the archives of this site to find discussion on Andy Grove's similar big project to use in silico to destroy traditional pharma discovery. We haven't heard much from that project since.
A waste for society? No. A waste for the people doing it who are never going to be appropriately compensated for the value of their failure to society? Yes.
The churn that arises as a consequence of the fact that we don't know how to reward failure (or even merely punish it less) is the real waste.
Yes, but if the scientific value capture problem were solved then science could scale appropriately rather than beg for charity.
I was (and would be) much more valuable to society as a structural biologist than I am as a software engineer, but the market disagrees so vehemently that it's cost prohibitive for me to fight it, and the result is that we all lose. The reason why it disagrees is not hard to understand and not particularly difficult to categorize as a market failure rather than a "hard truth." I can't really think of a good way to actually address the problem, though, so mostly this just amounts to venting.
He's on the admin / capital side of the equation and will do fine, I'm sure.
> paid competitively
That's not an endorsement and hardly an excuse. Unless things have changed dramatically in the last year or two, pay for computational / structural biologists is 1/2 to 1/3 of what a person with the same skills gets for helping build Uber for Poodles, which is in turn 1/2 to 1/3 of what a person with the same skills gets at AppleGooFaceZon or on Wall St.
The field, like many scientific fields, runs on passion, naivete, and green cards, and is quite abusive to the people doing the actual work, even though it showers adjacent concerns with money.
If science didn't suffer from such a severe value capture problem, scientists could win a seat at the table, but it does, so practicing scientists get shut out and the money goes instead to capital/risk, showmen (often "graduated" scientists themselves), lawyers, etc.
I didn't say that building better models didn't have a payoff. I'm saying this particular approach shows little to no evidence that it's truly revolutionary or even a marginal/incremental improvement over random guesses.
Building better models that simulate a human biological process must have some sort of payoff? It increases human knowledge, and should provide a foundation on which others can build.