Visualization of Prediction Basis of Chemical Compounds using Fingerprint

Abstract

In optimizing compound structures using machine learning, it is important not only to build and predict using highly accurate models, but also to obtain new knowledge that can be applied to material development. So far, methods for visualizing the prediction basis of compounds using deep learning have been reported, but the development of methods that can be applied to small data that is difficult to analyze by deep learning is also required. In this presentation, we propose a method to integrate the contribution of each substructure obtained by a machine learning model using fingerprint as the descriptor in consideration of the spread of the substructure and visualize it on the target molecule. We also show that the proposed method was applied to the published data and a reasonable prediction basis was obtained in comparison with known chemical findings. Furthermore, we report the results of using the proposed method for the optimization of copolymers.