5 ms·

“The performance of a 512-node Anton machine is over 17,000 nanoseconds of simulated time per day for a protein-water system consisting of 23,558 atoms.[5] In c

“The performance of a 512-node Anton machine is over 17,000 nanoseconds of simulated time per day for a protein-water system consisting of 23,558 atoms.[5] In comparison, MD codes running on general-purpose parallel computers with hundreds or thousands of processor cores achieve simulation rates of up to a few hundred nanoseconds per day on the same chemical system.” 17,000 ns of simulation per day sounds crazy small but I wonder how that compares to the timescale of molecular interactions. How helpful have Antons been to pharma research etc?

cing 7y ago
There's quite a nice plot from a review paper of D.E. Shaw Research that lists the timescale of several biological processes (and compares it to other experimental methods), https://www.annualreviews.org/doi/full/10.1146/annurev-biophys-042910-155245 https://www.annualreviews.org/doi/full/10.1146/annurev-bioph... (Figure 2). Anton has been extremely helpful for studying the basic science of protein dynamics in academia and has been applied in industry (namely at Relay Therapeutics), but drug discovery is a long process so we still haven't seen the fruits of those long simulations yet.
deepnotderp 7y ago
I don't think it's been very helpful, since computational modeling in general doesn't seem to be a cure all for drug discovery challenges
- Reelin 7y ago
  It probably would be a cure all, but the computers are still _way_ too slow!
unemphysbro 7y ago
Using highly optimized code for that system size, I used to get ~500 ns per day using a single 2080. Some references: 10^-3 ns - Hydrogen bond vibrations, 100+ ns - Protein side chains moving, 1000 - 10000 ns is the timescale of protein folding
- cma 7y ago
  This thing was made around the time of the nvidia 300 series.
tchitra 7y ago
[Disclaimer: I used to work at D. E. Shaw Research from 2011-2016] The early Anton 1 numbers of 17us/day on 100K atoms were huge leap forward then. At that time, GPU-based simulations (e.g. GROMACS/Desmond on GPU) were doing single digit ns/day. Remember, even for 'fast-folding' proteins, the relaxation time is on the order of us and you need 100s of samples before you can converge statistical properties, like folding rates [0]. Anton 2 got a 50-100x speed-up [1] which made it much easier to look at druggable pathways. Anton was also used for studying other condensed matter systems, such as supercooled liquids [2]. Your question of why is this so slow or small is prescient. On the reasons that we have to integrate the dynamical equations (e.g. Newtonian or Hamiltonian mechanics) at small, femtosecond timesteps (1 fs = 1e-15s) is because the vibrational frequencies of bonds are on the order of picoseconds (1 ps = 1e-12s). Given that you also have to compute Omega(n^2) pairwise interactions between n particles, you end up having a large runtime to get to ns and us while respecting bond frequencies. The hard part, for atomistic/all-atom simulation is that n is on the order of 1e5-1e6 for a single protein with 100s of water molecules. The water molecules are extremely important to simulate exactly since you need to get polar phenomena, such as hydrogen bonding, correct to get folded structure and druggable sites correct to angstrom precision (1e-10 meters). If you don't do atomistic simulations (e.g. n is much smaller and you ignore complex physical interactions, including semi-quantum interactions), you have a much harder time matching precision experiments. [0] https://science.sciencemag.org/content/334/6055/517 https://science.sciencemag.org/content/334/6055/517 [1] https://ieeexplore.ieee.org/abstract/document/7012191/ https://ieeexplore.ieee.org/abstract/document/7012191/ [the variance comes from the fact that different physics models and densities cause very different run times -> evaluating 1/r^6 vs. 1/r^12 in fixed precision is very different w.r.t communication complexity and Ewald times and FFTs and ...] [2] https://pubs.acs.org/doi/abs/10.1021/jp402102w https://pubs.acs.org/doi/abs/10.1021/jp402102w
- deleted 7y ago
  [deleted]
- madez 7y ago
  This explanation is interesting. Thanks for sharing it. While reading it, I got the impression that the simulation is not fully quantum mechanical, but rather classical with select quantum mechanical effects. Which parts of quantum mechanics are idealised away and how do we know that not including them won't significantly reduce the quality of the result? Are you possibly using stochastical noise in the simulations and repeat them multiple times, in the hope that whatever disturbance caused by the idealisation of the model is covered by the noise?

cing 7y ago

There's quite a nice plot from a review paper of D.E. Shaw Research that lists the timescale of several biological processes (and compares it to other experimental methods), https://www.annualreviews.org/doi/full/10.1146/annurev-biophys-042910-155245 https://www.annualreviews.org/doi/full/10.1146/annurev-bioph... (Figure 2). Anton has been extremely helpful for studying the basic science of protein dynamics in academia and has been applied in industry (namely at Relay Therapeutics), but drug discovery is a long process so we still haven't seen the fruits of those long simulations yet.

deepnotderp 7y ago

I don't think it's been very helpful, since computational modeling in general doesn't seem to be a cure all for drug discovery challenges

Reelin 7y ago
It probably would be a cure all, but the computers are still _way_ too slow!

unemphysbro 7y ago

Using highly optimized code for that system size, I used to get ~500 ns per day using a single 2080. Some references: 10^-3 ns - Hydrogen bond vibrations, 100+ ns - Protein side chains moving, 1000 - 10000 ns is the timescale of protein folding

cma 7y ago
This thing was made around the time of the nvidia 300 series.

tchitra 7y ago

[Disclaimer: I used to work at D. E. Shaw Research from 2011-2016] The early Anton 1 numbers of 17us/day on 100K atoms were huge leap forward then. At that time, GPU-based simulations (e.g. GROMACS/Desmond on GPU) were doing single digit ns/day. Remember, even for 'fast-folding' proteins, the relaxation time is on the order of us and you need 100s of samples before you can converge statistical properties, like folding rates [0]. Anton 2 got a 50-100x speed-up [1] which made it much easier to look at druggable pathways. Anton was also used for studying other condensed matter systems, such as supercooled liquids [2]. Your question of why is this so slow or small is prescient. On the reasons that we have to integrate the dynamical equations (e.g. Newtonian or Hamiltonian mechanics) at small, femtosecond timesteps (1 fs = 1e-15s) is because the vibrational frequencies of bonds are on the order of picoseconds (1 ps = 1e-12s). Given that you also have to compute Omega(n^2) pairwise interactions between n particles, you end up having a large runtime to get to ns and us while respecting bond frequencies. The hard part, for atomistic/all-atom simulation is that n is on the order of 1e5-1e6 for a single protein with 100s of water molecules. The water molecules are extremely important to simulate exactly since you need to get polar phenomena, such as hydrogen bonding, correct to get folded structure and druggable sites correct to angstrom precision (1e-10 meters). If you don't do atomistic simulations (e.g. n is much smaller and you ignore complex physical interactions, including semi-quantum interactions), you have a much harder time matching precision experiments. [0] https://science.sciencemag.org/content/334/6055/517 https://science.sciencemag.org/content/334/6055/517 [1] https://ieeexplore.ieee.org/abstract/document/7012191/ https://ieeexplore.ieee.org/abstract/document/7012191/ [the variance comes from the fact that different physics models and densities cause very different run times -> evaluating 1/r^6 vs. 1/r^12 in fixed precision is very different w.r.t communication complexity and Ewald times and FFTs and ...] [2] https://pubs.acs.org/doi/abs/10.1021/jp402102w https://pubs.acs.org/doi/abs/10.1021/jp402102w

deleted 7y ago
[deleted]
madez 7y ago
This explanation is interesting. Thanks for sharing it. While reading it, I got the impression that the simulation is not fully quantum mechanical, but rather classical with select quantum mechanical effects. Which parts of quantum mechanics are idealised away and how do we know that not including them won't significantly reduce the quality of the result? Are you possibly using stochastical noise in the simulations and repeat them multiple times, in the hope that whatever disturbance caused by the idealisation of the model is covered by the noise?