Verification of the neural network training process for spectrum-based chemical substructure prediction using metamorphic testing

Abstract

Fourier-transform infrared spectroscopy (FTIR) is one of the commonly used techniques in chemical analysis. The chemical bonds that are present in samples absorb infrared light at various wavelengths based on the properties of chemical bonds between sets of atoms bonded together. By extracting these absorbance patterns, we aim to predict the presence or absence of various substructures within a compound based on its FTIR spectrum. Hypothetically, a powerful machine learning method with enough examples of a substructure should be able to identify the structure of an unknown compound by analyzing its FTIR spectrum. To this extent we developed a novel system that trains neural networks to predict the presence of various substructures within a compound. We then propose to apply metamorphic testing to verify the network training process. Experimental results exhibit that metamorphic testing helps to develop a more effective training process for classifier neural networks.