Deep Q-Learning-Based Molecular Graph Generation for Chemical Structure Prediction From Infrared Spectra

Abstract

In this paper, we present a novel approach to predicting chemical structures from their infrared (IR) spectra using deep Q-learning. IR spectra measurements are widely used in chemical analysis because they provide information on the types and characteristics of chemical bonds present within compounds. However, there are currently no algorithms to predict the entire chemical structure of a broad range of compounds relying solely on IR spectra, unless there is an exact or closely matched spectrum in an existing reference spectra library. To address this, we apply double deep Q-learning for automated prediction of the entire chemical structures of organic compounds based on IR spectra. Our method builds predicted structures by starting from a single carbon atom and subsequently adding an atom and bond step-by-step by ranking the rewards of each possible addition based on Q-values. We devised new structural similarity metrics, atom bond count and substructure count metrics to achieve our goal. Compared to the commonly used structural similarity score, the Jaccard index of extended-connectivity fingerprints, the devised metrics exhibit more suitable properties for Q-learning. The deep Q-model, which uses the combination of our two proposed metrics, gives the overall best performance and can generate structures similar to the actual structures in terms of their structural features and molecular weight in most tested cases.