Therefore, ChatMolData has the potential to collect valuable structure-activity information from patents in bulk, complementing the ChEMBL database and scaling up the information for databases whenever we want. As shown in Figure &, some of the extracted bioactivity data is represented using different numbers of "+" symbols to indicate levels of activity. ChatMolData can convert this single-column bioactivity description into a more detailed multi-column format through natural language queries, using the Pro_Converse tool (as shown in the Supplementary Information Note 4). For instance, "++++" initially indicates an activity level of less than 10 nM. After receiving the query, the agent converts the data into three columns: one for the relation ("<"), one for the value ("10"), and one for the unit ("nM"). This task execution further enhances the compatibility of the structured data with the ChEMBL database, facilitating the subsequent data storage process. To our knowledge, the format of molecule-related patents is highly diverse, and sometimes the structure information is not shown within tables. Inspired by Morin's work , ChatMolData first uses the discriminative tool, Doc_Seg, to analyze the contents of the unstructured file, extract the molecular structure and save them in SMILES format, which distinguish between molecular structure, Markush structure and background. Then, it uses the Image2SMILES tool to extract all the structures. The example mentioned above is demonstrated in Supplementary Information Note 5.