Predicting Chemical Roles from Database Descriptions Using Natural Language Processing

Abstract In recent years, the use of document information to predict compound activity has gained attention. However, existing methods face challenges in applying these predictions to compounds not included in the training data because of the shortage and insufficient applicability of chemical knowledge as well as in interpreting the basis for predictions. Therefore, this study developed an approach to classifying the chemical roles of these compounds. By analyzing the descriptions of compounds registered in databases using Doc2Vec, we obtained the embeddings. Specifically, by setting the tag portion of Doc2Vec to the substructures of compounds, we acquired embeddings corresponding to the substructures and summed them to represent the compounds. We predicted the presence or absence of 10 chemical roles for the compounds; compared to predictions using chemical descriptors, our predictions had higher F1 scores for many chemical roles. Additionally, by applying Shapley additive explanations, we visualized the substructures likely to influence the prediction of the presence or absence of each role, confirming that their interpretation was consistent with the findings of the previous research. This paper suggests that the obtained embeddings, which combine the structural information and annotations of compounds, are useful descriptors for classifying the chemical roles of the compounds.