Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks

Nuclear magnetic resonance (NMR) spectroscopy is an indispensable tool for determining the structural characteristics of a molecule by analyzing its chemical shifts. A wealth of NMR spectra therefore exists and continues to amass on a daily basis, at an ever-increasing rate owing to the progressive...

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1. autor:	Shiyun Liu (5826171) (author)
Kolejni autorzy:	Jacqueline M. Cole (1301070) (author)
Wydane:	2025
Hasła przedmiotowe:	Biophysics Biochemistry Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified requires manual examination increasing rate owing data interpretation step practically best model nmr field strength methods could overcome h nmr spectra data analysis step new chemical compound determined molecular substructures 13 </ sup 1 </ sup cnn model emerged suitably trained scientist cnn model performance lstm model achieved recurrent neural network convolutional neural network nmr spectroscopy becoming c nmr spectra incorporating experimental metadata network model incorporating metadata chemical analysis lstm model cnn ), neural network nmr spectrum considering performance network architectures without incorporating molecular representation experimental conditions chemical shifts 311 experimental term memory study explores structural characterization structural characteristics spectral intensities slightly lower novel neighbor multilayer perceptron ml methods machine learning long short indispensable tool highest accuracy daily basis automatically correlate
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Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks

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