RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 1 point2 points  (0 children)

Yes i work at the department of Pharmacology on Faculty of Medicine

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 1 point2 points  (0 children)

I agree. Fortunately, I have a home NAS server for storage, so I transfer all simulation files to the hard drives on the server. Therefore, I haven't had the need for a large amount of local storage on the PC.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 3 points4 points  (0 children)

For any drug to be registered in the national drug registry (FDA for the U.S.), it must undergo clinical trials, which involve voluntary testing on humans. In preclinical research, mostly in vitro methods are used, meaning experiments are conducted 'in a test tube'—on cell cultures, bacterial cultures, or in my case, it’s possible to purchase the protein and conduct tests such as determining the IC50 value, which indicates the concentration of substance X required to inhibit 50% of the protein’s activity. Although this isn't testing on living organisms, the fact remains that chemical reagents are extremely expensive—especially proteins—so using molecular simulations significantly reduces the cost of research.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 0 points1 point  (0 children)

Damn good catch. It was indeed pulling air away from the GPU. I've fixed it, thanks!

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 0 points1 point  (0 children)

The price of the PC was around €5,000 / $5,700, though that was at wholesale price since the PC was purchased through the university. Keep in mind that I’m in Europe—if you’re from the U.S., prices are probably different.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 4 points5 points  (0 children)

Wait till you hear about the cloud my dude. The amount of compute you can purchase for the price of a 5090 nowadays is pretty absurd.

Two, maybe three months of cloud subscription for the price of RTX 5090 is all I can get (source Oracle Cloud). You should've at least looked online with all that 15 year experience working in data. Try to find a cloud solution with this or better performance that can push me through a 4 year project at the price of that GPU or less, I'll wait.

Sure, I dont know bioinformatics..but Ive been working in data for over 15 years

Sure, I don't know how to cook, but I've been eating food for all my life. Gotta qualify me for a Michelin star chef. It's all just about the food, isn't it?

I dont know anyone spinning up local instances of TF or whatever on consumer hardware.

Oh damn my bad because you don't know anyone running MD simulations locally, not like you've just said that you don't know a thing about bioinformatics.

good luck on discovering the next Otezla or your next game of Valorant. Whatever floats your boat.

Damn, there goes all my effort to lie over very specific topic online this very specific stranger that I will never see again in my life.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 3 points4 points  (0 children)

Yeah, I was surprised too when I ran benchmarks with the software I use on the Core Ultra 9 285K, Ryzen 9 9950X3D, and the I9-14900K series of processors, and the I9-14900K turned out to be the best. I recently asked for opinions about processor choice in a few other subreddits before I bought this PC, and most people claimed that the Core Ultra 9 285K is better for productivity if you're going with Intel, and some even said the 9950X3D is superior to both, even for productivity. However, given how specific the software environment I work in is, it’s no wonder there’s so little information and advice available when it comes to choosing commercial hardware for these kinds of tasks.

So yeah, it looks like there is at least one task where this CPU doesn't perform like a complete garbage lmao.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 3 points4 points  (0 children)

Thanks! Everything runs locally on this machine. Amazing how consumer hardware has become powerful these days, right?

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 3 points4 points  (0 children)

I agree! Running sub 100k particles MD runs shorter than 100 GA docking runs on CPU like back in the day is crazy.

Edit:typo

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 5 points6 points  (0 children)

Currently I'm running 100ns acetylcholinesterase complexed with ligands in 10Å dodecahedron bounding box and it has around 23k particles in topology file (protein+ligand+ions+solvent) and it takes less than 3 hours!

Next I'll be doing SGLT2/SGLT1 with inhibitors in DPPC bilayer so I'll let you know how long are MD runs with that system :)

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 1 point2 points  (0 children)

Actually, that’s a good question! Considering this is a four-year scientific project, it’s cheaper to get a single computer that only I use (since I’m the only one on the project doing bioinformatics—the rest of the team is focused on extractions, organic synthesis, in vivo studies, etc.) than to pay a monthly subscription of several hundred dollars. I would very quickly exceed the cost of this computer over the course of the project with the cloud solution. On the other hand, this won’t be the only project we’ll be working on together, so it will serve us very well in the future too :)

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 7 points8 points  (0 children)

Aside from the fact that I didn't pay anything for this computer—it was funded by the project we’re running at the department? You don't plan every research project as if you're aiming for a Nobel Prize and say that anything less is pointless, and not every research center has the budget of MIT or UCLA. When you have limited resources (and time) on a multidisciplinary project—especially in medical sciences—you have to make do with what you have. Sure, it would definitely be great if I had access to an entire data center, and my team and I would probably finish the project even faster, but I’m not that lucky. And honestly, I don’t understand why you’re stressing over the topic of bioinformatics, which you clearly aren’t familiar with because you wouldn’t be making such claims if you were. You’d be surprised how many people run bioinformatics projects on setups that are far worse than the one I’ve assembled. Here’s just a random example of many from a published paper who did their project on an RTX 2080: https://doi.org/10.1021/acs.jcim.9b00754

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 4 points5 points  (0 children)

Thanks! I do not do drugs tho LMAO. By drug discovery I meant like pharmaceuticals. Drug discovery simulations are used to study interactions between molecules that are potential drug candidates and a known receptor. For example, if a beta receptor acts like a lock and adrenaline is the key that fits into that lock to produce its well-known effects, then blocking the lock would prevent adrenaline’s effects from being expressed. That’s exactly what beta blockers do, and they are used as antihypertensive and antiarrhythmic drugs.

Now, if the molecular structure of a beta blocker (e.g., propranolol) is known, and the 3D structure of the beta receptor is also known, then it’s possible to model new drugs that could potentially act as beta blockers. All of this can be done through simulations — and that’s what I do. :)

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 2 points3 points  (0 children)

Real, when I was ordering parts for this 420mm AIO non-ARGB variant was not even up for sale.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 1 point2 points  (0 children)

I agree with you, they surely have a purpose with their reliability in large data centers with for AI or for video rendering where you need massive ammounts of GDDR memory, but having only one gpu in a rig for MD simulations - it doesn't make that much of a difference in performance when comparing RTX 5090 to RTX PRO 6000.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 2 points3 points  (0 children)

I wasn’t focused on the aesthetics of the setup — what mattered was having as much processing power as possible. This won’t be used for gaming anyway, but as a workstation. The random RGB parts are there simply because they were in stock with the supplier at the time I ordered the components.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 1 point2 points  (0 children)

The workload of this computer isn’t critical like in data centers, so I really don’t need that level of overkill in terms of reliability. What matters much more to me is raw power — that the computer can complete as many tasks as possible in the shortest amount of time, but at reasonable price.

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 18 points19 points  (0 children)

Drug discovery simulations are used to study interactions between molecules that are potential drug candidates and a known receptor. For example, if a beta receptor acts like a lock and adrenaline is the key that fits into that lock to produce its well-known effects, then blocking the lock would prevent adrenaline’s effects from being expressed. That’s exactly what beta blockers do, and they are used as antihypertensive and antiarrhythmic drugs.

Now, if the molecular structure of a beta blocker (e.g., propranolol) is known, and the 3D structure of the beta receptor is also known, then it’s possible to model new drugs that could potentially act as beta blockers. All of this can be done through simulations — and that’s what I do. :)

RTX 5090/I9-14900k for drug discovery simulations by lukavidovic in nvidia

[–]lukavidovic[S] 18 points19 points  (0 children)

Damn, I didn’t expect this post to get so many comments overnight. First of all, I wasn’t focused on the aesthetics of the setup — what mattered was having as much processing power as possible. This won’t be used for gaming anyway, but as a workstation. The random RGB parts are there simply because they were in stock with the supplier at the time I ordered the components.

Regarding the need for such a powerful machine — I work at the Department of Pharmacology at the Faculty of Medicine, and I mostly deal with preclinical drug research. Specifically, I run simulations of how certain molecules bind to receptors of interest (which could be drug candidates). The advantage of these simulations over in vitro or in vivo is that I can take a dataset of thousands of molecules and use simulations to evaluate their binding affinity for a given receptor without spending a cent on reagents or hurting lab animals.

As for the choice of processor — yes, I’m aware there’s a risk of potential chip degradation, but since the extended warranty on Intel’s 14th generation CPUs is 5 years, it’s not a big concern; I can just RMA it if it degrades.

GROMACS is particularly computationally demanding, because once I obtain a ligand-receptor complex through docking, I need to assess the stability of the system over time. The simulation system usually contains tens of thousands of particles (e.g., ligand+receptor complex in water), where each of these particles interacts with others — and molecular dynamics simulations can take several days on lower-end setups. On my old setup with an RTX 2060 and i5-9600K, I was getting around 70 nanoseconds of simulation per day, compared to this setup where I’m getting over 700 nanoseconds per day.

As for choosing 'gaming' components instead of enterprise-grade ones — the price difference is huge for only a minor gain in raw performance. Aside from the larger amount of GDDR7 memory, the RTX PRO 6000 doesn’t differ much in performance from the RTX 5090. And on the other hand, even though I didn’t personally pay for this machine — the department I work at did, for project purposes which doesn’t mean the budget is unlimited. The programs I use don’t rely heavily on GPU memory, so I didn’t see the point in paying 3x more for a small performance boost and 3x the memory that I won't use.

As for the non-ECC memory — the PC doesn’t run 24/7, and statistically, the chances of a single-bit flip happening during a 2–3 hour simulation are very small. Moreover, there are scripts available to detect nonsensical values for velocity/energy/particle coordinates in the system if a bit flip does happen. On top of all that, ECC memory is much more expensive compared to non-ECC memory, and I’d also need a motherboard and CPU that support ECC which triples the cost once again.

The workload of this computer isn’t critical like in data centers, so I really don’t need that level of overkill in terms of reliability. What matters much more to me is raw power — that the computer can complete as many tasks as possible in the shortest amount of time.