Literature DB >> 34537128

Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering.

E William Webb1, Peter J H Scott2.   

Abstract

Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic. In this perspective, the authors offer insights into how the technology could be applied to improve the radiosynthesis of new radiopharmaceuticals for PET imaging, including identification of an optimal labeling approach as well as strategies for radiolabeling reaction optimization.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Copper-mediated radiofluorination; Positron emission tomography; Radiochemistry; Radiolabeling

Mesh：

Substances：

Year:  2021        PMID： 34537128      PMCID： PMC9168959          DOI： 10.1016/j.cpet.2021.06.012

Source DB:  PubMed          Journal:  PET Clin        ISSN： 1556-8598


  24 in total

Review 1.  Synthesis of 11C, 18F, 15O, and 13N radiolabels for positron emission tomography.

Authors:  Philip W Miller; Nicholas J Long; Ramon Vilar; Antony D Gee
Journal:  Angew Chem Int Ed Engl       Date:  2008       Impact factor: 15.336

2.  Simultaneous solvent screening and reaction optimization in microliter slugs.

Authors:  Brandon J Reizman; Klavs F Jensen
Journal:  Chem Commun (Camb)       Date:  2015-09-04       Impact factor: 6.222

3.  Intermolecular reaction screening as a tool for reaction evaluation.

Authors:  Karl D Collins; Frank Glorius
Journal:  Acc Chem Res       Date:  2015-02-20       Impact factor: 22.384

Review 4.  Computer-Assisted Synthetic Planning: The End of the Beginning.

Authors:  Sara Szymkuć; Ewa P Gajewska; Tomasz Klucznik; Karol Molga; Piotr Dittwald; Michał Startek; Michał Bajczyk; Bartosz A Grzybowski
Journal:  Angew Chem Int Ed Engl       Date:  2016-04-08       Impact factor: 15.336

5.  Predicting reaction performance in C-N cross-coupling using machine learning.

Authors:  Derek T Ahneman; Jesús G Estrada; Shishi Lin; Spencer D Dreher; Abigail G Doyle
Journal:  Science       Date:  2018-02-15       Impact factor: 47.728

6.  Machine Learning in Computer-Aided Synthesis Planning.

Authors:  Connor W Coley; William H Green; Klavs F Jensen
Journal:  Acc Chem Res       Date:  2018-05-01       Impact factor: 22.384

7.  Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds.

Authors:  Aaron M Virshup; Julia Contreras-García; Peter Wipf; Weitao Yang; David N Beratan
Journal:  J Am Chem Soc       Date:  2013-05-02       Impact factor: 15.419

8.  Synthesis and Preclinical Evaluation of 11C-UCB-J as a PET Tracer for Imaging the Synaptic Vesicle Glycoprotein 2A in the Brain.

Authors:  Nabeel B Nabulsi; Joël Mercier; Daniel Holden; Stephane Carré; Soheila Najafzadeh; Marie-Christine Vandergeten; Shu-Fei Lin; Anand Deo; Nathalie Price; Martyn Wood; Teresa Lara-Jaime; Florian Montel; Marc Laruelle; Richard E Carson; Jonas Hannestad; Yiyun Huang
Journal:  J Nucl Med       Date:  2016-02-04       Impact factor: 10.057

9.  Total Radiosynthesis: Thinking outside "the box".

Authors:  Steven H Liang; Neil Vasdev
Journal:  Aust J Chem       Date:  2015-08-28       Impact factor: 1.321

10.  A Design of Experiments (DoE) Approach Accelerates the Optimization of Copper-Mediated 18F-Fluorination Reactions of Arylstannanes.

Authors:  Gregory D Bowden; Bernd J Pichler; Andreas Maurer
Journal:  Sci Rep       Date:  2019-08-06       Impact factor: 4.379
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  1 in total

1.  SNAr Radiofluorination with In Situ Generated [18F]Tetramethylammonium Fluoride.

Authors:  So Jeong Lee; María T Morales-Colón; Allen F Brooks; Jay S Wright; Katarina J Makaravage; Peter J H Scott; Melanie S Sanford
Journal:  J Org Chem       Date:  2021-09-10       Impact factor: 4.198
  1 in total

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