Issue 18, 2020

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 the 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 are poorly characterised 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.

Graphical abstract: Spectral deep learning for prediction and prospective validation of functional groups

Supplementary files

Article information

Article type
Edge Article
Submitted
10 Гру 2019
Accepted
13 Бер 2020
First published
13 Бер 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2020,11, 4618-4630

Spectral deep learning for prediction and prospective validation of functional groups

J. A. Fine, A. A. Rajasekar, K. P. Jethava and G. Chopra, Chem. Sci., 2020, 11, 4618 DOI: 10.1039/C9SC06240H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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