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Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups

preprint
revised on 24.01.2020 and posted on 24.01.2020 by Jonathan Fine, Anand Rasjashekar, Krupal P. Jethava, Gaurav Chopra

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.

Funding

Integrative Data Science Initiative

History

Email Address of Submitting Author

gchopra@purdue.edu

Institution

Purdue University

Country

USA

ORCID For Submitting Author

0000-0003-0942-7898

Declaration of Conflict of Interest

None

Version Notes

version-2 with experimental data

Licence

Exports