I don't see how this conception of personalized medicine would work. For many drugs, the binding sites aren't terribly different or different at all between individuals. I can see this being different for larger populations. More likely you'll see drugs created that very specifically target something so that the personalization will be at the level of the drug cocktail as opposed to synthesizing some new molecule. This isn't to mention the possibly wildly different effects you can get from changing something seemingly minor about the molecule. A sort of good example of this is methamphetamine (yes, I know chirality isn't minor but its a good layman's example).

How does that happen without $1 billion worth of test requirements from the FDA?

Failed attempts. The regulatory framework in the US and Europe is still pretty good at protecting patients.

You can call me naive, or you can read my papers. For example, https://www.nature.com/articles/nchem.1821 is a massive simulation of a GPCR that my team designed and ran. It's a similar idea to the Relay work (simulate the dynamics of the protein, make a markov model of the subsets with transition parameters) With that tech, we could easily implement computational mutagenesis. I agree 100% that protein engineering is a great technology and I wish we could do it rationally. But to be honest, none of my computational work can beat what Jim Wells did at Genentech in the late 90s (converting subtilisin to subtiligase through amino acid mutations).

Here's another paper I wrote, https://www.ncbi.nlm.nih.gov/pubmed/24265211 which demonstrates there was a systematic error in protein force field implementations; our work was a major breakthrough in improving structure prediction.

Also, being able to predict structures isn't sufficient to engineer protein function.

Please don't call people naive; especially if they are experts in their field.

Relay is not doing protein engineering or working on predicting protein structure. They are making models of protein dynamics to assist in drug discovery (often using already determined structures). We both disagree with the parent commenter that it's a waste, and to claim that Murcko and D.E. Shaw are going in "blindly" would be ignoring decades of research on protein dynamics of some of the hardest drug targets out there. The fact remains that there aren't many success stories of using simulations of protein dynamics to accelerate drug discovery. Computational chemistry protocols used routinely in pharma drug discovery typically do not include this type of detail.

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...

[1] https://github.com/PatWalters/Free-Wilson

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.

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.

I wouldn't really say the HGP taught us a lot about protein evolution. The original few genomes were snapshots of indivduals or amalgams. We do know a bit more about evolution from sequencing many more people (post-HGP), but it hasn't really been super-productive compared to previous work (note, my postdoc work was on protein functional evolution) from before the HGP.

For example, Carl Woese, using only 16s RNA from a wide range of species and mostly hand-computed similarity clustering, managed to find a previously unrecognized kingdom. I don't think we've really had any truly revolutionary discoveries like that from HGP and post-HGP sequencing that focuses on humans.

The single hard problem in pharma is predicting which drugs will get through to full approval, and how much it will cost from initial results.

If you could do that reliably, it would completely transform pharma.

The real cost occurs in Phase 2 clinical trials, where 70 % of promising-looking compounds turn out to be no better than placebo. The body is a complex system with a huge number of enzymes and feedback circuits, this outcome isn't much of a surprise then.

You can't blame the FDA. But thanks to Trump's "right-to-try" phase 2 will be a smaller hurdle now. Good luck, let's see how costs develop.

interesting point. though this ignores the (surprisingly) successful re-purposing strategies to reduce future regulatory burden and and reduce or even eliminate high throughput screening campaigns. that well will dry up sooner or later but other discovery platforms ie phage display etc. have become commoditized and robust enough that they are pretty accessible to even low capitalized startups if finding a ligand is what you need to get off the ground.

You can do a screen with a CRO for low six figures