Brooke E. Husic | DeepAI

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∙ 12/14/2022

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

A generalized understanding of protein dynamics is an unsolved scientifi...

1 Maciej Majewski, et al. ∙

∙ 10/28/2021

Deeptime: a Python library for machine learning dynamical models from time series data

Generation and analysis of time-series data is relevant to many quantita...

323 Moritz Hoffmann, et al. ∙

∙ 06/14/2021

Machine Learning Implicit Solvation for Molecular Dynamics

Accurate modeling of the solvent environment for biological molecules is...

0 Yaoyi Chen, et al. ∙

∙ 07/22/2020

Coarse Graining Molecular Dynamics with Graph Neural Networks

Coarse graining enables the investigation of molecular dynamics for larg...

14 Brooke E. Husic, et al. ∙

∙ 04/16/2019

Kernel canonical correlation analysis approximates operators for the detection of coherent structures in dynamical data

We illustrate relationships between classical kernel-based dimensionalit...

0 Stefan Klus, et al. ∙

∙ 11/28/2018

Variational Selection of Features for Molecular Kinetics

The modeling of atomistic biomolecular simulations using kinetic models ...

6 Martin K. Scherer, et al. ∙

∙ 07/12/2018

Amplifying state dissimilarity leads to robust and interpretable clustering of scientific data

Existing methods that aim to automatically cluster data into physically ...

2 Brooke E. Husic, et al. ∙

∙ 03/12/2018

Spatial Graph Convolutions for Drug Discovery

Predicting the binding free energy, or affinity, of a small molecule for...

0 Evan N. Feinberg, et al. ∙

∙ 12/20/2017

Unsupervised learning of dynamical and molecular similarity using variance minimization

In this report, we present an unsupervised machine learning method for d...

0 Brooke E. Husic, et al. ∙

∙ 11/23/2017

Variational Encoding of Complex Dynamics

Often the analysis of time-dependent chemical and biophysical systems pr...

0 Carlos X. Hernández, et al. ∙

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