IS GATE.IO DOWN
Recently, supramolecular hydrogel received considerable attention and promised many useful applications in medicine, food chemistry, and nanotechnology. However, it is impossible to predict a hydrogelator solely based on the molecular structure, which limits the research of supramolecular hydrogel. Herein, we utilized data mining to construct a hydrogel dataset with 2,669 positive samples. Via the deep learning method, we synthesized and found 9 molecules that can form hydrogel in certain conditions. In general, we public 2,678 hydrogelators in HYDROGEL-DB. We hope this database not only serves as depository, but also can become a wiki of hydrogel for the researchers who are interested in exploring the potential of supramolecular hydrogelator to address societal needs on various fronts. At this point, we are expecting researchers can share with the community the hydrogel data they found, or published. To anyone who is going to share data, please let us know (xzeng@foxmail.com or Jeff-Shi@hnu.edu.cn), and we will update this database.