DeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and <sup>13</sup>C NMR spectra-Reference-Cited by-同舟云学术

DeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and ¹³C NMR spectra

Published:2024 Issue:4 Volume:3 Page:818-829
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Devata Sriram¹^ORCID,Sridharan Bhuvanesh¹,Mehta Sarvesh¹,Pathak Yashaswi¹,Laghuvarapu Siddhartha¹^ORCID,Varma Girish²^ORCID,Priyakumar U. Deva¹^ORCID

Affiliation:

1. Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India

2. Center for Security, Theory and Algorithms Research, International Institute of Information Technology, Hyderabad, India

Abstract

DeepSPInI is a deep reinforcement learning method that predicts the molecular structure when given infrared and 13C nuclear magnetic resonance spectra with an accuracy of 91.5%.

Funder

Science and Engineering Research Board

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D4DD00008K

Reference79 articles.

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2. J. G.Smith , Mass Spectrometry and Infrared Spectroscopy , Organic Chemistry , McGraw-Hill , 5th edn, 2016 , ch. 13

3. E.Brian and B. F. T.Mann , 13C NMR data for organometallic compounds , Academic Press , 1981

4. The Theory of NMR – Chemical Shift , University of Colorado, Boulder, Chemistry and Biochemistry Department , 2011

5. A framework for automated structure elucidation from routine NMR spectra

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