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.
Learning molecular dynamics with simple language model built upon long short-term memory neural network
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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.