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Spectral deep learning for prediction and prospective validation of functional groups

Abstract

State-of-the-art identification of the functional groups present in an unknown chemical entity requires expertise of a skilled spectroscopist to analyse and interpret Fourier Transform Infra-Red (FTIR), Mass Spectroscopy (MS) and/or Nuclear Magnetic Resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that poorly characterized in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection.

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Supplementary files

Article information


Submitted
10 Dec 2019
Accepted
13 Mar 2020
First published
13 Mar 2020

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2020, Accepted Manuscript
Article type
Edge Article

Spectral deep learning for prediction and prospective validation of functional groups

J. Fine, A. A. Rajasekar, K. P. Jethava and G. Chopra, Chem. Sci., 2020, Accepted Manuscript , DOI: 10.1039/C9SC06240H

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