Spectral deep learning for prediction and prospective validation of functional groups.

Chemical Science
Jonathan FineGaurav Chopra

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.

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Citations

Jul 10, 2021·The Journal of Chemical Physics·Julia WestermayrReinhard J Maurer
Mar 27, 2021·Analytica Chimica Acta·Laureen CoicEric Ziemons
Nov 3, 2020·Computational and Structural Biotechnology Journal·Yotsawat PomyenSakda Khoomrung
Jul 1, 2021·Analytical Chemistry·Abigail A EndersHeather C Allen
Jul 16, 2020·Journal of Chemical Information and Modeling·Joshua Schrier
Sep 11, 2021·Physical Chemistry Chemical Physics : PCCP·Shuqian YeXi Zhu

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Methods Mentioned

BETA
nuclear magnetic resonance
SPLASH

Software Mentioned

SMARTS
SPLASH
RDKit
Python

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