k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations-Reference-Cited by-同舟云学术

k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations

Published:2024-06-21 Issue:13 Volume:20 Page:5583-5597
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Chen Lexin¹²,Roe Daniel R.³^ORCID,Kochert Matthew⁴⁵,Simmerling Carlos⁵⁴⁶^ORCID,Miranda-Quintana Ramón Alain¹²^ORCID

Affiliation:

1. Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States

2. Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States

3. Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States

4. Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States

5. Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States

6. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States

Funder

National Institutes of Health

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00308

Reference63 articles.

1. Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms

2. Unsupervised Learning Methods for Molecular Simulation Data

3. Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important

4. Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

5. Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble