A Large-Scale Database for Chemical Structure Recognition and Preliminary Evaluation

Chemical structure recognition (CSR), transforming chemical structure images into formulas in character strings (such as SMILES), is a challenging problem due to the complex 2D structures and relationships. For this research, there is not a database of sufficient scale and diversity for model design and fair evaluation. In this paper, we present a large-scale chemical structure database named CASIA-CSDB, containing 480,668 samples (images corresponding to SMILES strings). To construct the database, we select chemical structures from the ChEMBL, a well-known bioactive molecules database, and use the RDKit tool to generate images according to the chemical format SMILES strings. The selected structures represent the major types of chemical compounds covering eight weight partitions. We also select a subset of 97,309 samples of the database to form the Mini-CASIA-CSDB database. To provide a benchmark, we evaluate three state-of-the-art image-to-markup recognition methods on the database. The results demonstrate the challenge of the database. The database with its annotation is available at http://www.nlpr.ia.ac.cn/databases/CASIA-CSDB/index.html.