{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:09:16Z","timestamp":1772118556190,"version":"3.50.1"},"reference-count":15,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2019,1,7]],"date-time":"2019-01-07T00:00:00Z","timestamp":1546819200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["P50GM107618"],"award-info":[{"award-number":["P50GM107618"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Comput Chem"],"published-print":{"date-parts":[[2019,3,15]]},"abstract":"<jats:p>\n                    The conversion of polymer parameterization from internal coordinates (bond lengths, angles, and torsions) to Cartesian coordinates is a fundamental task in molecular modeling, often performed using the natural extension reference frame (NeRF) algorithm. NeRF can be parallelized to process multiple polymers simultaneously, but is not parallelizable along the length of a single polymer. A mathematically equivalent algorithm, pNeRF, has been derived that is parallelizable along a polymer's length. Empirical analysis demonstrates an order\u2010of\u2010magnitude speed up using modern GPUs and CPUs. In machine learning\u2010based workflows, in which partial derivatives are backpropagated through NeRF equations and neural network primitives, switching to pNeRF can reduce the fractional computational cost of coordinate conversion from over two\u2010thirds to around 10%. An optimized TensorFlow\u2010based implementation of pNeRF is available on GitHub at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/aqlaboratory\/pnerf\">https:\/\/github.com\/aqlaboratory\/pnerf<\/jats:ext-link>\n                    \u00a9 2018 Wiley Periodicals, Inc.\n                  <\/jats:p>","DOI":"10.1002\/jcc.25772","type":"journal-article","created":{"date-parts":[[2019,1,8]],"date-time":"2019-01-08T15:16:10Z","timestamp":1546960570000},"page":"885-892","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Parallelized Natural Extension Reference Frame: Parallelized Conversion from Internal to Cartesian Coordinates"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6817-1322","authenticated-orcid":false,"given":"Mohammed","family":"AlQuraishi","sequence":"first","affiliation":[{"name":"Department of Systems Biology Harvard Medical School  Boston Massachusetts 02115"},{"name":"Laboratory of Systems Pharmacology Harvard Medical School  Boston Massachusetts 02115"}]}],"member":"311","published-online":{"date-parts":[[2019,1,7]]},"reference":[{"key":"e_1_2_6_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.20237"},{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1021\/jp509136y"},{"key":"e_1_2_6_3_1","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1017\/CBO9780511609633","volume-title":"Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods","author":"Marx D.","year":"2009"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-381270-4.00019-6"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1039\/C6SC05720A"},{"key":"e_1_2_6_6_1","author":"Jumper J. M.","year":"2017","journal-title":"bioRxiv"},{"key":"e_1_2_6_7_1","unstructured":"M.Ragoza L.Turner D. R.Koes arXiv:1710.07400 [cs q\u2010bio Stat] 2017."},{"key":"e_1_2_6_8_1","unstructured":"D.Masters C.Luschi arXiv:1804.07612 [cs Stat] 2018."},{"key":"e_1_2_6_9_1","unstructured":"M.Bayati J. P.Bardhan M.Leeser in2015IEEE High Performance Extreme Computing Conference (HPEC); 2015."},{"key":"e_1_2_6_10_1","unstructured":"Mart\u00edn Abadi A.Agarwal P.Barham E.Brevdo Z.Chen C.Citro G. S.Corrado A.Davis J.Dean M.Devin S.Ghemawat I.Goodfellow A.Harp G.Irving M.Isard Y.Jia R.Jozefowicz L.Kaiser M.Kudlur J.Levenberg D.Man\u00e9 R.Monga S.Moore D.Murray C.Olah M.Schuster J.Shlens B.Steiner I.Sutskever K.Talwar P.Tucker V.Vanhoucke V.Vasudevan F.Vi\u00e9gas O.Vinyals P.Warden M.Wattenberg M.Wicke Y. Yu X.Zheng TensorFlow: Large\u2010Scale Machine Learning on Heterogeneous Systems.2015."},{"key":"e_1_2_6_11_1","first-page":"1","volume":"18","author":"Baydin A. 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