187 citations · 210 across the 4 of their papers we have counts for
5 papers
Deep learning models for predicting RNA degradation via dual crowdsourcing
Hannah K. Wayment-Steele, Wipapat Kladwang, Andrew M. Watkins +26
Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limite…
Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation
Hannah K. Wayment-Steele, Vijay S. Pande
As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurre…
Transferable neural networks for enhanced sampling of protein dynamics
Mohammad M. Sultan, Hannah K. Wayment-Steele, Vijay S. Pande
Variational auto-encoder frameworks have demonstrated success in reducing complex nonlinear dynamics in molecular simulation to a single non-linear embedding. In this work, we illu…
Variational Encoding of Complex Dynamics
Carlos X. Hernández, Hannah K. Wayment-Steele, Mohammad M. Sultan +2
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual feature…
Investigating the role of boundary bricks in DNA brick self-assembly
Hannah Wayment-Steele, Daan Frenkel, Aleks Reinhardt
In the standard DNA brick set-up, distinct 32-nucleotide strands of single-stranded DNA are each designed to bind specifically to four other such molecules. Experimentally, it has…