The two sections above respectively demonstrate cases of extracting molecular data from structured and unstructured data, and the final data is saved in CSV or XLSX files for researchers to use. In this section, we will demonstrate ChatMolData is capable of analyzing molecular data in CSV or XLSX files by various tools within its DataFrame Module. As seen in Figure , ChatMolData demonstrates how to predict molecular properties from a CSV file and ultimately visualize the property distribution. In Figure , after the user submits the corresponding query, the ChatMolData selects the CMD_preprocess tool as Action1, aiming to clean the input molecular data by removing invalid SMILES and converting molecular SMILES into canonical SMILES, as depicted in Figure . In the next action, ChatMolData takes the cleaned molecular data as input and uses the CMD_Pred tool to predict multiple properties for the molecules in the dataset. According to the query requirements, the predicted properties include molecular weight (MW), lipophilicity (ALogP), topological polar surface area (tPSA), and Quantitative Estimate of Druglikeness (QED). As shown in Figure , the predicted properties expand the original table, and the new table is stored as a new file. At last, Action3 corresponds to the final query requirement, where the CMD_plotting tool is used to display the distribution of multiple properties within the molecular set. As illustrated in Figure , The LMA returns four histograms that depict MW, ALogP, tPSA, and QED, respectively. Regarding the value of this case, firstly, ChatMolData prioritizes the use of preprocessing tool to clean the data for CSV or XLSX molecular data files, which not only saves users' effort but also prevents program errors in subsequent operations. Secondly, CMD_Pred tool ensures that users quickly obtain files containing predicted molecular properties, relieving experimenters from the burden of manually inputting and querying each molecule individually. Furthermore, the agent's use of the CMD_plotting tool allows the generated molecular properties to be intuitively displayed to the user, avoid token limitation when only texts as input try to describe the overall distribution of a molecular dataset. These multimodal outputs can be served as an important reference for future new molecular design. Last but not least, the above case can be combined with the data extraction from databases or PDF data respectively mentioned earlier. The extracted or collected molecules can then be subjected to the processing operations described here, with the integration of two procedures facilitating more complex molecular data processing tasks. Accordingly, the successful execution of this task, by the multimodal agent, helps researchers gain a deeper understanding of the characteristics of molecular datasets. In the example mentioned above, an internal RDKit model is selected as the predictor. Because RDKit's internal functions provide an efficient method for calculating properties such as MW, ALogP, tPSA, and QED, and it has been frequently used as a benchmark in other performance comparisons. In recent years, various predictors based on deep neural networks have demonstrated outstanding performance in predicting bioactivity, toxicity, and material-related properties . To expand the agent's application in property prediction, several excellent models in property prediction have also been incorporated into ChatMolData as tools, such as continuous and data-driven descriptor (CDDD) for prediction of biological activities, multitask graph attention (MGA) for predicting toxicity properties, and Attentive FP for predicting the electronic properties for molecular semiconductors. Examples of these property predictions are shown in the Supplementary Information Note 6-8. The results suggest that ChatMolData is capable of effectively utilizing deep learning models to accurately predict molecular properties as well as visualizing the property distributions.