nano-GPT, a two-pass GPT model with scheduled sampling, predicts long-timescale molecular dynamics from short simulation windows and matches slow folding times for the Fip35 WW domain more closely than LSTM.
Symplectic splitting methods for rigid body molecular dynamics
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Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by nano-GPT
nano-GPT, a two-pass GPT model with scheduled sampling, predicts long-timescale molecular dynamics from short simulation windows and matches slow folding times for the Fip35 WW domain more closely than LSTM.