Yinghui Wen

Abstract Supramolecular hydrogels hold significant potential in drug delivery and tissue engineering, with standing out for their unique properties. Despite their promise, predicting nucleoside bioactivity remains challenging. This study aims to predict the biological activity of nucleosides to guide the rational synthesis of hydrogels.

1 Introduction Periodontitis, recognized by the World Health Organization as one of the three major oral diseases, is the sixth most prevalent chronic disease in the world, and affects 90% of the global population [1, 2]. Its clinical manifestations include chronic inflammation of periodontal tissues, which leads to the destruction of supporting structures such as gums, periodontal ligaments, and alveolar bone. It can even result in tooth mobility and tooth loss [3, 4].

Abstract Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives.