A fast local embedded boundary method suitable for high power electromagnetic sources

Autor: Mathialakan Thavappiragasam, Andrew Christlieb, John Luginsland, Pierson Guthrey

Publikováno v: AIP Advances, Vol 10, Iss 11, Pp 115318-115318-14 (2020)

High power sources of electromagnetic energy often require complicated structures to support electromagnetic modes and shape electromagnetic fields to maximize the coupling of the field energy to intense relativistic electron beams. Geometric fidelit

Externí odkaz: https://doaj.org/article/e1f59a2a4bea4d66b111bdd9410d9e3d

High-throughput virtual laboratory for drug discovery using massive datasets

Autor: Scott LeGrand, Mathialakan Thavappiragasam, Jens Glaser, Andreas F. Tillack, Matthew B. Baker, Oscar Hernandez, Josh V. Vermaas, Aaron Scheinberg, David M. Rogers, Ada Sedova, Jeff Larkin, Swen Boehm

Publikováno v: The International Journal of High Performance Computing Applications. 35:452-468

Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9a79c7febfe5a67c8e4436b62f2cd6d3
https://doi.org/10.1177/10943420211001565

OpenMDlr: parallel, open-source tools for general protein structure modeling and refinement from pairwise distances

Autor: Russell B Davidson, Jess Woods, T Chad Effler, Mathialakan Thavappiragasam, Julie C Mitchell, Jerry M Parks, Ada Sedova

Publikováno v: Bioinformatics. 38:3297-3298

Summary Easy-to-use, open-source, general-purpose programs for modeling a protein structure from inter-atomic distances are needed for modeling from experimental data and refinement of predicted protein structures. OpenMDlr is an open-source Python p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d005659d271ed8868df73b77cea1ad04
https://doi.org/10.1093/bioinformatics/btac307

Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Autor: Wael R. Elwasif, Mathialakan Thavappiragasam, Aaron Scheinberg, Ada Sedova, Oscar Hernandez

Publikováno v: P3HPC@SC
2020 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC)

Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18ab71d2616593d43e6a6c02038a2eec
https://doi.org/10.1109/p3hpc51967.2020.00009

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

Autor: Mathialakan Thavappiragasam, Gilchan Park, Kendall G. Byler, Leighton Coates, Laura Zanetti-Polzi, Jeffrey M. Larkin, Junqi Yin, John A. Gunnels, Omar Demerdash, Loukas Petridis, Ada Sedova, Carlos Soto, Aaron Scheinberg, Mai Zahran, Scott LeGrand, Jens Glaser, Jerome Baudry, Stephan Irle, Samuel Yen-Chi Chen, Andrey Kovalevsky, Isabella Daidone, Julie C. Mitchell, Arvind Ramanathan, Connor J. Cooper, Duncan Poole, V. Q. Vuong, Diogo Santos-Martins, David M. Rogers, Shinjae Yoo, Y. Shen, Oscar Hernandez, A. Tsaris, Swen Boehm, Debsindhu Bhowmik, Travis J Lawrence, Daniel W. Kneller, Shih-Hsien Liu, Jeremy C. Smith, Line Pouchard, Matthew B. Baker, Stefano Forli, Sally R. Ellingson, Anna Pavlova, Rupesh Agarwal, Micholas Dean Smith, Atanu Acharya, James C. Gumbart, Andreas F. Tillack, John D. Eblen, Josh V. Vermaas, Jerry M. Parks

Publikováno v: Journal of chemical information and modeling 60 (2020): 5832–5852. doi:10.1021/acs.jcim.0c01010
info:cnr-pdr/source/autori:Acharya A.; Agarwal R.; Baker M.B.; Baudry J.; Bhowmik D.; Boehm S.; Byler K.G.; Chen S.Y.; Coates L.; Cooper C.J.; Demerdash O.; Daidone I.; Eblen J.D.; Ellingson S.; Forli S.; Glaser J.; Gumbart J.C.; Gunnels J.; Hernandez O.; Irle S.; Kneller D.W.; Kovalevsky A.; Larkin J.; Lawrence T.J.; Legrand S.; Liu S.-H.; Mitchell J.C.; Park G.; Parks J.M.; Pavlova A.; Petridis L.; Poole D.; Pouchard L.; Ramanathan A.; Rogers D.M.; Santos-Martins D.; Scheinberg A.; Sedova A.; Shen Y.; Smith J.C.; Smith M.D.; Soto C.; Tsaris A.; Thavappiragasam M.; Tillack A.F.; Vermaas J.V.; Vuong V.Q.; Yin J.; Yoo S.; Zahran M.; Zanetti-Polzi L./titolo:Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19/doi:10.1021%2Facs.jcim.0c01010/rivista:Journal of chemical information and modeling/anno:2020/pagina_da:5832/pagina_a:5852/intervallo_pagine:5832–5852/volume:60
Journal of Chemical Information and Modeling
ChemRxiv
article-version (number) 1
article-version (status) pre

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79326e7712c0be5402b46e8c6ef2b9fd