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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...
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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...
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06/14/2021
Machine Learning Implicit Solvation for Molecular Dynamics
Accurate modeling of the solvent environment for biological molecules is...
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07/22/2020
Coarse Graining Molecular Dynamics with Graph Neural Networks
Coarse graining enables the investigation of molecular dynamics for larg...
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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...
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11/28/2018
Variational Selection of Features for Molecular Kinetics
The modeling of atomistic biomolecular simulations using kinetic models ...
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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 ...
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03/12/2018
Spatial Graph Convolutions for Drug Discovery
Predicting the binding free energy, or affinity, of a small molecule for...
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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...
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11/23/2017