Deep Convolutional Neural Networks for predicting highest priority functional group in organic molecules

TL;DR

This paper presents a deep convolutional neural network approach to predict the highest priority functional group in organic molecules from FTIR spectra, outperforming traditional machine learning methods like SVM.

Contribution

The study introduces a CNN-based model for functional group prediction from FTIR spectra, demonstrating superior performance over existing ML techniques.

Findings

CNN outperforms SVM in accuracy

Model effectively identifies dominant functional groups

Deep learning enhances spectroscopic analysis

Abstract

Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In the presence of multiple functional groups, the dominant functional group determines the compound's properties. Fourier-transform Infrared spectroscopy (FTIR) is a commonly used spectroscopic method for identifying the presence or absence of functional groups within a compound. We propose the use of a Deep Convolutional Neural Networks (CNN) to predict the highest priority functional group from the Fourier-transform infrared spectrum (FTIR) of the organic molecule. We have compared our model with other previously applied Machine Learning (ML) method Support Vector Machine (SVM) and reasoned why CNN outperforms it.