Flow chemistry, automation, robotics, and artificial intelligence (AI) tools promise to accelerate chemical synthesis processes underlying the discovery and development of molecules. Opportunities and challenges are highlighted through selected case studies, beginning with the integration of ML algorithms for multi-property prediction, molecular generation, CASP, and automated analytical quantification using an automated 96-well-plate platform that encompasses liquid handling, multistep chemical synthesis, isolation, and optical and chemical characterization. The discovery of new organic dye molecules and histone deacetylase inhibitors exemplifies the potential of such an autonomous molecular discovery platform. A subsequent study combines the text extraction and coding capabilities of large language models (LLMs) with predictions of machine learning models (MLs) to identify reactive electrochemical oxidation transformation and their optimal reaction conditions. Overall, the presented case studies aim to demonstrate that the integration of automation, modularity, robotics, and AI/ML techniques enhances our ability to perform flow and batch experiments through idea generation, experimental design, execution, optimization, and autonomy.
