Unsupervised Learning Methods for Molecular Simulation Data von Glielmo, Aldo, Husic, Brooke E, Rodriguez, Alex, Clementi, Cecilia, Noé, Frank, Laio, Alessandro

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Machine learning coarse-grained potentials of protein thermodynamics von Majewski, Maciej, Pérez, Adrià, Thölke, Philipp, Doerr, Stefan, Charron, Nicholas E., Giorgino, Toni, Husic, Brooke E., Clementi, Cecilia, Noé, Frank, De Fabritiis, Gianni

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MSMBuilder: Statistical Models for Biomolecular Dynamics von Harrigan, Matthew P., Sultan, Mohammad M., Hernández, Carlos X., Husic, Brooke E., Eastman, Peter, Schwantes, Christian R., Beauchamp, Kyle A., McGibbon, Robert T., Pande, Vijay S.

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PotentialNet for Molecular Property Prediction von Feinberg, Evan N, Sur, Debnil, Wu, Zhenqin, Husic, Brooke E, Mai, Huanghao, Li, Yang, Sun, Saisai, Yang, Jianyi, Ramsundar, Bharath, Pande, Vijay S

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Modeling the mechanism of CLN025 beta-hairpin formation von McKiernan, Keri A., Husic, Brooke E., Pande, Vijay S.

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Optimized parameter selection reveals trends in Markov state models for protein folding von Husic, Brooke E., McGibbon, Robert T., Sultan, Mohammad M., Pande, Vijay S.

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Markov State Models: From an Art to a Science von Husic, Brooke E, Pande, Vijay S

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Simultaneous coherent structure coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity von Husic, Brooke E, Schlueter-Kuck, Kristy L, Dabiri, John O

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Machine learning implicit solvation for molecular dynamics von Chen, Yaoyi, Krämer, Andreas, Charron, Nicholas E., Husic, Brooke E., Clementi, Cecilia, Noé, Frank

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Coarse graining molecular dynamics with graph neural networks von Husic, Brooke E., Charron, Nicholas E., Lemm, Dominik, Wang, Jiang, Pérez, Adrià, Majewski, Maciej, Krämer, Andreas, Chen, Yaoyi, Olsson, Simon, de Fabritiis, Gianni, Noé, Frank, Clementi, Cecilia

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Identification of simple reaction coordinates from complex dynamics von McGibbon, Robert T., Husic, Brooke E., Pande, Vijay S.

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Variational selection of features for molecular kinetics von Scherer, Martin K., Husic, Brooke E., Hoffmann, Moritz, Paul, Fabian, Wu, Hao, Noé, Frank

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Ward Clustering Improves Cross-Validated Markov State Models of Protein Folding von Husic, Brooke E, Pande, Vijay S

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Kernel methods for detecting coherent structures in dynamical data von Klus, Stefan, Husic, Brooke E., Mollenhauer, Mattes, Noé, Frank

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Note: MSM lag time cannot be used for variational model selection von Husic, Brooke E., Pande, Vijay S.

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Variational encoding of complex dynamics von Hernández, Carlos X, Wayment-Steele, Hannah K, Sultan, Mohammad M, Husic, Brooke E, Pande, Vijay S

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Deeptime: a Python library for machine learning dynamical models from time series data von Hoffmann, Moritz, Scherer, Martin, Hempel, Tim, Mardt, Andreas, de Silva, Brian, Husic, Brooke E, Klus, Stefan, Wu, Hao, Kutz, Nathan, Brunton, Steven L, Noé, Frank

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A Minimum Variance Clustering Approach Produces Robust and Interpretable Coarse-Grained Models von Husic, Brooke E, McKiernan, Keri A, Wayment-Steele, Hannah K, Sultan, Mohammad M, Pande, Vijay S

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Deflation reveals dynamical structure in nondominant reaction coordinates von Husic, Brooke E., Noé, Frank

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Understanding how Beta-Hairpins Fold using Molecular Dynamics Simulations in Multiple Force Fields von Husic, Brooke E., McKiernan, Keri A., Pande, Vijay S.

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