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Computational Tools for Chemical Rea...
~
University of California, Irvine.
Computational Tools for Chemical Reactions : = Simulation & Prediction.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Computational Tools for Chemical Reactions :/
Reminder of title:
Simulation & Prediction.
Author:
Fooshee, David.
Description:
1 online resource (106 pages)
Notes:
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Subject:
Computer science. -
Online resource:
click for full text (PQDT)
ISBN:
9780355413984
Computational Tools for Chemical Reactions : = Simulation & Prediction.
Fooshee, David.
Computational Tools for Chemical Reactions :
Simulation & Prediction. - 1 online resource (106 pages)
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Thesis (Ph.D.)--University of California, Irvine, 2017.
Includes bibliographical references
Achieving human-level performance at predicting chemical reactions remains an open prob- lem with broad potential applications. Here we describe a deep learning-based tool for chemical reaction prediction and product identification. Significant efforts were made to curate and refine a new, high-quality data set of hand-selected chemical reactions written at the level of elementary electron movements. Using deep artificial neural networks trained on this data, we demonstrate a high degree of accuracy at predicting real-world reactions. Because predictions are made at the elementary step level, they can be chained together to form multi-step reaction pathway searches, to help identify unknown side products.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355413984Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
Computational Tools for Chemical Reactions : = Simulation & Prediction.
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Fooshee, David.
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Computational Tools for Chemical Reactions :
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Simulation & Prediction.
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1 online resource (106 pages)
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Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
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Adviser: Pierre F. Baldi.
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Thesis (Ph.D.)--University of California, Irvine, 2017.
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Includes bibliographical references
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Achieving human-level performance at predicting chemical reactions remains an open prob- lem with broad potential applications. Here we describe a deep learning-based tool for chemical reaction prediction and product identification. Significant efforts were made to curate and refine a new, high-quality data set of hand-selected chemical reactions written at the level of elementary electron movements. Using deep artificial neural networks trained on this data, we demonstrate a high degree of accuracy at predicting real-world reactions. Because predictions are made at the elementary step level, they can be chained together to form multi-step reaction pathway searches, to help identify unknown side products.
520
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We also present a computational brewing application, COBRA, capable of simulating com- plex chemical mixtures. We demonstrate its efficacy at modeling both the photooxidation of isoprene, and the oxidation of squalene in the presence of ozone, by comparing predicted results with results obtained from high-resolution mass spectrometry.
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In addition, we address the problem of atom-mapping for chemical reactions, by designing a new atom-mapping algorithm that can be used to annotate unmapped reactions.
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Ann Arbor, Mich. :
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ProQuest,
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2018
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Mode of access: World Wide Web
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Computer science.
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University of California, Irvine.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10634473
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click for full text (PQDT)
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