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.
A variational approach to modeling slow processes in stochastic dynamical systems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-bio.QM 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.