[dekhn]

I think it's still an open question whether you truly need an ensemble of long simulations (versus many shorter ones that can be generated in embarassingly parallel mode on GPUs).

Nonetheless, I'm thankful for both DESMOND and Anton, which helped push the MD community out of its moribund state in the early 2000s. I still don't think MD simulations produce anything that exceeds the opportunity value of the power they consume, and it seems unclear that they ever will, although I still would love to know the answer to the question: "could sophisticated classic forcefields with reasonable O(nlogn) scaling every be truly valuable as a virtual screening feature generator/evaluation mechanism".

[tchitra]

I agree that it is still unsolved, from a practical perspective. I think both sides (the MD vs. ensemble sampling) have incorporated techniques that the other side uses to improve sampling efficiency and accuracy. At the same time, I suspect that the sampling methodology only works with some form of structured guidance, whether it be MD or embedding (a la AlphaFold, which has a few former DESRES people working on it). The raw Monte Carlo methods that people use for 'embarrassing' parallelism often have terrible scaling — the spectral gaps of the Markov chains are abysmal and you only realize that after 1000s of core-hours.

On the other hand, DESRES had been focusing on a lot of acceleration methods that involved hardware optimized HMC-esque methodologies that had reasonably good parallelism. AFAICT the only public description of this work is in the appendices of this paper [0].

At the end of the day, you probably need both techniques because the pure sampling approaches lose fine structure (e.g. binding pockets opening up with anomalous frequency due to water clusters) whereas the standalone MD model has too long of a decorrelation time to get averages to converge.

[0] https://www.pnas.org/content/116/10/4244