187 citations · 232 across the 5 of their papers we have counts for
6 papers
DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries
Kirill Shmilovich, Benson Chen, Theofanis Karaletsos +1
DNA-Encoded Library (DEL) technology has enabled significant advances in hit identification by enabling efficient testing of combinatorially-generated molecular libraries. DEL scre…
Using Deep Learning for Segmentation and Counting within Microscopy Data
Carlos X. Hernández, Mohammad M. Sultan, Vijay S. Pande
Cell counting is a ubiquitous, yet tedious task that would greatly benefit from automation. From basic biological questions to clinical trials, cell counts provide key quantitative…
Automated design of collective variables using supervised machine learning
Mohammad M. Sultan, Vijay S. Pande
Selection of appropriate collective variables for enhancing sampling of molecular simulations remains an unsolved problem in computational biophysics. In particular, picking initia…
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…
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan +2
We present a machine learning framework for modeling protein dynamics. Our approach uses L1-regularized, reversible hidden Markov models to understand large protein datasets genera…