Machine Learning (ML) is a set of techniques used by computers to perform a specific task without an explicit set of instructions provided by the user. ML techniques have been successfully applied to multiple chemical problems in recent years, and still show promise for the advancement of several areas of chemistry. Popular machine learning architectures, such as Random Forest , Multiple Layer Perception , Generalized Adversarial Networks , and Recurrent Neural Networks have been used on chemical data for small-molecule design , metabolism , toxicology , photo-electric properties, solubility, and retrosynthesis . It has been shown that direct molecule as a subgraph of groups of atoms (i.e., functional groups) has distinct advantages over fingerprinting methods . The representation of a molecule or dataset can be reduced to a lower-dimensional latent space by using an autoencoder . Here, we also used an encoder to create a corresponding latent space based on spectra to predict functional groups which may also be useful to design molecules for specific spectral properties.