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Let’s look at another aspect of the general computational enthusiasm we’re seeing in drug discovery these days. As you read my opinions, though, keep in mind that I’ve seen many cycles of this in a >30 year career, so I inevitably be informed (or perhaps misinformed?) by that experience. But not many of us have thirty years thinking about the subject of this press release from Novo Nordisk (NGOs), announcing their foray into quantum computing.
This part of the blog post is where the background on that concept should go, but I’m obviously not qualified to deliver it. In short, it’s easy enough to figure out – it’s the application of computational methods to phenomena that can occur meaningfully at the quantum level, things like entanglement, superposition, and interference. There is no one thing that covers all possible quantum computing techniques (as far as I know!) but all the proposals plan to use these effects to do things that are not possible here in big world. You hear a lot of hand-waving talk about that step, things about “doing all the calculations at once” and so on, and from what I know on the subject the better that you don’t ignore that. But it seems certain that quantum-based algorithms exist (or may exist) that can provide significant advantages over what can be done classically. You’re not breaking out of the Church-Turing thesis, though: anything a non-quantum computer can do, in theory, a quantum one (of any kind) can do, and vice versa. But there are opportunities for many accelerations in how quickly results can be obtained.
Actually knowing the advantages, however – that’s difficult. There was a claim of “quantum supremacy” from Google (GOOG, GOOGL) not long ago (a result obtained more efficiently than any classical system could), but not everyone in the field believed that, to put it mildly. , and the new results confirm that doubt. Building the hardware for a working quantum computer is very difficult, as is keeping it working once you’ve built it. And no matter how well you build it, you have to deal with quantum decoherence: the idealized particle-in-a-box in quantum chemistry courses can sit there platonically and not interact with its surroundings, but that is not the case. here in the real world, especially the real world where you want to read the states of all these qubits at the end of the process. You gradually (or maybe not gradually) lose the specialty of the quantum says that you went to such a problem to get, so a big part of any working quantum computer can be some really strong error correction techniques.
Another big part of any such system is the software that makes the most of the quantum technology you use. That’s not simple either. There are some algorithms that have proven to be very good at mixing quantum phenomena (I discussed some of them in this post, which I think was the last time I participated in this topic), but it is safe to say that a lot of effort has gone into it. going to identify more such techniques assuming we have the hardware to run them at some point. And that brings us, finally, to Novo Nordisk!
The press release announced an effort “to establish the first full-scale quantum computer for the development of new drugs“, but as you will see if you read, that’s not all. We quickly learned that this machine is also expected to provide breakthroughs in the study of climate change and the “green transition”. In fact , the whole thing is changing. into a dump-truck delivery of buzzy phrases. Personalized medicine? Yes! Human microbiome studies? Of course! Big data in genomics? Naturally! Bag sustainable materials, decarbonization, cybersecurity, energy solutions – it’s all there. And to be fair, of course, one can imagine that more and more capable computing resources can be used in all fields. credit to Novo Nordisk and the Danish government, this is a twelve-year initiative, the first seven of which are expected to be spent just figuring out what kind of quantum computer to build, which seems at least realistic.
But in each case, you run into the same basic problem: faster computers only help processes. whose rate-limiting stop includes the speed of computation. That may seem obvious or tautological at first, but think about it. This all goes back to the same things I always say about (for example) AlphaFold – that as great as it is, it won’t lead to an immediate revolution in drug discovery because our biggest problems aren’t will depend on knowledge of protein structure and thus cannot be facilitated by it. Similarly, having something that can sort through huge mounds of (say) genomics data would be useful – but even more useful is understanding what those results mean and what should they do?. That’s a slow process, for sure. Look at that AlphaFold-driven work I wrote about last week: if you suddenly gave the team access to a working quantum computer, what would they be able to do with it under current conditions? Generate reliable docking-and-scoring results faster? What did you buy? (Please note – I’m not putting down that paper or the people who wrote it! If you read it, you’ll feel their own frustrations with the state of computing tools we have today, and I’m sure The just facilitating those who are not the first thing they want).
So there are, ultimately, several important choices to make when you start talking about quantum computing for the life sciences. First, you need to decide what kind of quantum computational technique you are going to use. Then you have to decide how to figure it out in the actual hardware. After that, you need to find the algorithms that will take advantage of your new machine. Then you need to carefully choose the problems where those algorithms fit the best, and finally, you want to find problems that await the types of results that you can provide. What we are left with after these selection steps will become clear over time. And effort. And lots and lots of money.
Disclosure: nothing
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Editor’s note: The summary bullets for this article were selected by the editors of Seeking Alpha.
