1 code implementation • 14 Oct 2021 • Hannah K. Wayment-Steele, Wipapat Kladwang, Andrew M. Watkins, Do Soon Kim, Bojan Tunguz, Walter Reade, Maggie Demkin, Jonathan Romano, Roger Wellington-Oguri, John J. Nicol, Jiayang Gao, Kazuki Onodera, Kazuki Fujikawa, Hanfei Mao, Gilles Vandewiele, Michele Tinti, Bram Steenwinckel, Takuya Ito, Taiga Noumi, Shujun He, Keiichiro Ishi, Youhan Lee, Fatih Öztürk, Anthony Chiu, Emin Öztürk, Karim Amer, Mohamed Fares, Eterna Participants, Rhiju Das
Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models.
no code implementations • 17 Mar 2018 • Hannah K. Wayment-Steele, Vijay S. Pande
We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.
no code implementations • 2 Jan 2018 • Mohammad M. Sultan, Hannah K. Wayment-Steele, Vijay S. Pande
In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification that allows us to rapidly perform sampling in multiple related systems.
2 code implementations • 23 Nov 2017 • Carlos X. Hernández, Hannah K. Wayment-Steele, Mohammad M. Sultan, Brooke E. Husic, Vijay S. Pande
Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds.