Enabling scalable and accurate clustering of distributed ligand geometries on supercomputers-Reference-Cited by-同舟云学术

Enabling scalable and accurate clustering of distributed ligand geometries on supercomputers

Published:2017-04 Issue: Volume:63 Page:38-60
ISSN:0167-8191
Container-title:Parallel Computing
language:en
Short-container-title:Parallel Computing

Author:

Zhang Boyu,Estrada Trilce,Cicotti Pietro,Balaji Pavan,Taufer Michela

Funder

NSF

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference41 articles.

1. Computational multi-scale modeling in protein-ligand docking;Taufer;IEEE Eng. Med. Biol. Mag.,2009

2. VMD – visual molecular dynamics;Humphrey;J. Mol. Graph.,1996

3. Chimera: a model and language for active DOOD systems;Ceri,1994

4. Automatic selection of near-native protein-ligand conformations using a hierarchical clustering and volunteer computing;Estrada,2010

5. Accurate analysis of large datasets of protein-ligand binding geometries using advanced clustering methods;Estrada;Comput. Biol. Med.,2012

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Large deformation, high energy density dielectric elastomer actuators: Principles, factors, optimization, applications, and prospects;Chemical Engineering Journal;2024-06

2. A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2021-07-01

3. Parallel and scalable Dunn Index for the validation of big data clusters;Parallel Computing;2021-05

4. A survey of algorithms for transforming molecular dynamics data into metadata for in situ analytics based on machine learning methods;Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences;2020-01-20

5. Graphic Encoding of Macromolecules for Efficient High-Throughput Analysis;Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics;2018-08-15