Brooke E. Husic

TL;DR: An overview of the MSM field to date is presented, presented for a general audience as a timeline of key developments in the field, and the current frontiers of methods development are highlighted, as well as exciting applications in experimental design and drug discovery.

TL;DR: The PotentialNet family of graph convolutions, specifically designed for and achieve state-of-the-art performance for protein–ligand binding affinity, is presented and a cross-validation strategy based on structural homology clustering is introduced that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.

TL;DR: The MSMBuilder package is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change and includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis.

TL;DR: The use of a time-lagged VAE, or variational dynamics encoder (VDE), to reduce complex, nonlinear processes to a single embedding with high fidelity to the underlying dynamics and how the VDE is able to capture nontrivial dynamics in a variety of examples.

TL;DR: This Review provides a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicates likely directions for further developments in the field.