The document discusses the evolution of data for AI models in drug discovery, highlighting the significance of public data, particularly the ChEMBL database, which contains extensive medicinal chemistry data useful for AI innovations. It details the current initiatives, such as the DeepADMET project, aimed at improving data quality and accessibility for drug development. Additionally, the future outlook emphasizes the importance of various assays and advanced data integration techniques to enhance predictive modeling in drug discovery.